Tesis: "Object Based Image Analysis (OBIA)"

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Ortega-García, José Antonio. "Forest stand delineation through remote sensing and Object-Based Image Analysis". Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-28005.

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Forest stand delineation is an essential task of forest management planning which can be time consuming and exposed to subjectivity. The increasing availability of LiDAR data and multispectral imagery offers an opportunity to improve stand delineation by means of remotely-sensed data. Under these premises, ASTER imagery and low-density LiDAR data have been used to automatically delineate forest stands in several forests of Navarra (Spain) through Object-Based Image Analysis (OBIA). Canopy cover, mean height and the canopy model have been extracted from LiDAR data and, along with VNIR ASTER bands, introduced in OBIA for forest segmentation. The outcome of segmentation has been contrasted, on the one hand, assessing segments’ inner heterogeneity. On the other, OBIA’s segments and existing stand delineations have been compared with a new method of geometrical fitting which has been ad hoc designed for this study. Results suggest that low-density LiDAR and multispectral data, along with OBIA, are a powerful tool for stand delineation. Multispectral images have a limited predicting utility for species differentiation and, in practical terms, they help to discriminate between broad-leaved, conifer and mixed stands. The performance of ASTER data, though, could be improved with higher spatial resolution VNIR imagery, specifically sub-metric VNIR orthophotos. LiDAR data, in contrast, offers a great potential for forest structure depiction. This perspective is connected with the increasingly higher resolution datasets which are to be provided by public institutions and the rapid development of drone technology. Complexity of OBIA may limit the use of this technique for small consulting firms but it is an advisable instrument for companies and institutions involved in major forestry projects.
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Chima, C. I. "Monitoring and modelling of urban land use in Abuja Nigeria, using geospatial information technologies". Thesis, Coventry University, 2012. http://curve.coventry.ac.uk/open/items/286e264c-3d26-4448-8049-6f2ef3fda727/1.

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This thesis addresses three research gaps in published literature. These are, the absence of Object Based Image Analysis (OBIA) methods for urban Land Use and Land Cover (LULC) analysis in Nigeria; the inability to use Nigeriasat-1 satellite data for urban LULC analysis and monitoring urban growth in Nigeria with Shannon’s Entropy Index. Using Abuja as a case study, this research investigated the nature of land use/land cover change (LULCC). Specific objectives were: design of an object based classification method to extract urban LULC; validate a method to extract LULC in developing countries from multiple sources of remotely sensed data; apply the method to extract LULC data; use the outputs to validate an Urban Growth Model (UGM); optimise an UGM to represent patterns and trends and through this iterative process identify and prioritise the driving forces of urban change; and finally use the outputs of the land use maps to determine if planning has controlled land use development. Landsat 7 ETM (2001), Nigeriasat-1 SLIM (2003) and SPOT 5 HRG (2006) sensor data were merged with land use cadastre in OBIA, to produce land use maps. Overall classification accuracies were 92%, 89% and 96% respectively. Post classification analysis of LULCC indicated 4.43% annual urban spread. Shannon’s Entropy index for the study period were 0.804 (2001), 0.898 (2003) and 0.930 (2006). Cellular Automata/Markov analysis was also used to predict urban growth trend of 0.89% per annum. For the first time OBIA has been used for LULC analysis in Nigeria. This research has established that Nigeriasat-1 data can contribute to urban studies using innovative OBIA methods. In addition, that Shannon’s Entropy Index can be used to understand the nature of urban growth in Nigeria. Finally, the drivers of LULCC in Abuja are similar to those of planned capital cities in other developing economies. Land use developments in Abuja can provide an insight into urban dynamics in a developing country’s capital region. OBIA, Shannon’s Entropy Index and UGM can aid urban administrators and provide information for sustainable urban planning and development.

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Inomata, Takeshi, Flory Pinzón, José Luis Ranchos, Tsuyoshi Haraguchi, Hiroo Nasu, Juan Carlos Fernandez-Diaz, Kazuo Aoyama y Hitoshi Yonenobu. "Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala". MDPI AG, 2017. http://hdl.handle.net/10150/624959.

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The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 x 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic.

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Pedrassoli, Júlio César. "Análise orientada a objeto para detecção de favelas e classificação do uso do solo em Taboão da Serra/SP". Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/8/8135/tde-03052012-085635/.

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O crescimento acelerado das cidades e os reflexos desse aumento das populações urbanas é preocupação constante na atualidade. Nesse processo, o surgimento de ocupações precárias, especialmente nas regiões metropolitanas, torna-se uma das características mais explicitas, caracterizando a própria lógica de ocupação, uso e direito desigual ao território. O monitoramento dessas áreas, sua formação e expansão, são uma necessidade crescente, em diversos locais no mundo, visto que a inclusão dessas áreas à cidade formal é tido como gatilho para a melhoria das condições de vida de mais de 100 milhões de pessoas que vivem em favelas no mundo todo, como colocam as Metas de Desenvolvimento do Milênio propostas pela Organização das Nações Unidas. Contudo, para que os habitantes das favelas sejam atendidos em seu direito a uma vida digna, faz-se necessário seu conhecimento e principalmente quantas são e onde estão. Um importante instrumento, com relação benéfica entre tempo de aquisição, custo de aplicação e possibilidade de replicabilidade e transferência de conhecimento é o uso de dados de Sensoriamento Remoto. Estes possibilitam o estabelecimento de metodologias através de procedimentos de detecção de feições e classificação do uso do solo, para identificação dessas áreas. Não obstante, os métodos de classificação clássicos quando aplicados a imagens de altíssima resolução espacial não conseguem extrair de forma satisfatória, em determinados casos, informações para uso intraurbano. Nesse ínterim surgem novos paradigmas de classificação de imagens como a Análise Orientada a Objeto, onde o processo de classificação parte do objeto geográfico definido a partir da segmentação da imagem, aproximando o objeto de feições do mundo real. Sobre estes objetos é possível a aplicação de regras de pertinência e de contexto através de linguagens e softwares específicos que permitem a transposição do conhecimento humano de fotointerpretação relação contextual para o meio computacional. Este trabalho objetivou avaliar o uso desta técnica de classificação para a detecção e mapeamento de favelas no município de Taboão da Serra/SP, utilizando dados auxiliares para a caracterização destas áreas e seus graus e tipos de precariedade. Os resultados demonstram a validade da aplicação da técnica.
The accelerated growth of the cities and the reflections of the increase of the urban population has been a constant concern nowadays. In this process, the occurrence of precarious occupancies, mainly in the metropolitan regions, has become one of the most explicit characteristics, describing the logic of occupancy itself, unequal use and right to the territory. The monitoring of these areas, their lineup and expansion, are an increasing need in several places in the world, as the inclusion of these areas in the formal city is considered a trigger for the living conditions improvement of over 100 million people who live in slums all over the world, as the Developments Goals of the Millennium proposed by the United Nations Organization. However, in order to meet the rights to a dignified life of the slums inhabitants, it is necessary to know about them mainly their number and where they are. An important tool related to the beneficial relation among the acquisition time, application cost and possibility of applying again, and transference of knowledge is the use of data from Remote Sensing. These data make it possible to establish the methodologies through the detection of features procedures and classification of the land use for these areas identification. Nevertheless the classical methods of classification cannot obtain, in certain cases, information on the interurban use, in a satisfactory way. In the interim, new paradigms of images classification appear like the Object Based Image Analysis (OBIA) which goes from the defined geographic object to the image segmentation, approaching the object to features of the real world. The application of pertinent rules and context over these objects is possible through specific languages and softwares that allow the transference of human knowledge of photo interpretation and contextual relation to the computing environment. This work aimed at evaluating the use of this classification technique for detection and zoning of slums in Taboão da Serra/SP town using supporting data for the areas characterization, its grades and kinds of precarious conditions. The results show the validity of the technique application.

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Martinová, Olga. "Extrakce krajinných prvků z dat dálkového průzkumu". Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2013. http://www.nusl.cz/ntk/nusl-226365.

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In this thesis, an approach to automatically derive information about land cover from the remotely sensed data is presented. The data interpretation was done with classification process and performed in software eCognition Developer. The Object-based image analysis, which assignes the classes - for example land cover types, to clusters of pixels (=objects), was used. For the classification, products of two different data sources were combined - the orthophotos generated from aerial imagery and Normalized Digital surface model derived from LiDAR data. Five types of landscape elements were identified and classified.

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Lübker, Tillmann. "Object-based remote sensing for modelling scenarios of rural livelihoods in the highly structured farmland surrounding Kakamega Forest, western Kenya". Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-150628.

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This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences). Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods. The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type. As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research
Die vorliegende Arbeit untersucht räumlich-expliziten das stark strukturierte und dicht besiedelte Agrarland um den Kakamega Wald (Westkenia). Dabei kombiniert der interdisziplinäre Ansatz Methoden und Technologien verschiedener Wissenschaftsbereiche: die Fernerkundung mit der objekt-basierten Bildanalyse (OBIA), GIS und die räumlich-explizite Modellierung (Geoinformatik und Geographie) mit sozio- und agro-ökonomische Aspekten (Human- und Sozialwissenschaft) sowie der Kartographie. Zudem steht die Arbeit in Bezug zum Schutz der biologischen Vielfalt (Biologie). Ausgehend von einer Referenzdatenerfassung vor Ort und einer visuellen Bildinterpretation wurden räumlich sehr hochauflösende QuickBird-Satellitenbilddaten, die 466 km² des Agrarlandes abdecken, mit Hilfe von OBIA ausgewertet. In einem integrativen Ansatz wurden dabei statistische Verfahren und Expertenwissen kombiniert, um einen ausgefeilten Regelsatz zur Klassifizierung zu erzeugen. Das Klassifizierungsergebnis unterscheidet 15 Klassen der Landnutzung bzw. -bedeckung; zusammen mit zeitlich extrapolierten und räumlich neu verteilten Bevölkerungsdaten sowie sozio- und agro-ökonomischen Faktoren ermöglichte es, eine räumlich-explizite Typologie des Agrarlandes zu erstellen und Szenarien zum ländlichen Auskommen zu modellieren. Die Agrarlandtypologie unterscheidet zehn Landtypen: 3 Zuckerrohr-dominierte Typen (48% des Gebietes), 3 Tee-dominierte Typen (30%), 2 Übergangstypen (15%), 1 Typ steilen Geländes (2%) und 1 zentralen Typ (5%). Die Szenarien betrachten mögliche zukünftige Entwicklungen der Erträge und Preise der Hauptanbauarten Zuckerrohr, Tee und Mais. Von allen Agrarlandtypen ist der „marginal Zuckerrohr-dominierte Typ“ am besten gerüstet, um zukünftigen Problemen zu begegnen. Bezeichnend für diesen Typ sind – neben einer vergleichsweise geringen Bevölkerungsdichte – ein hoher Anteil an Nahrungsmittelanbau zusammen mit einem gemäßigten Anbau von exportorientierten Agrarprodukten. Als Teil der Forschungsarbeit werden verschiedene neuartige Methoden vorgestellt, u.a. ein neuer konzeptioneller Rahmen für das Kategorisieren von Studien zur Parameteroptimierung, die „area fitness rate“ (AFR) als neue Messgröße für Flächendiskrepanzen, die klassifikations-basierte Nächster-Nachbar Klassifizierung sowie ein Ansatz zum Bestimmen der Güte von OBIA-Klassifizierungen. Schließlich gibt die Arbeit eine Reihe von Empfehlungen und bietet vielversprechende Ausgangspunkte für weiterführende wissenschaftliche Forschungen

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Marpu, Prashanth Reddy. "Geographic object-based image analysis". Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola&quot, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:105-5519610.

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The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments.

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Turcat, Jean-Philippe. "Object-based content representation and analysis for image retrieval". Thesis, Staffordshire University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394142.

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Volotão, Carlos Frederico de Sá. "Image segmentation using IHS space and object-based analysis". Instituto Nacional de Pesquisas Espaciais (INPE), 2013. http://urlib.net/sid.inpe.br/mtc-m19/2013/01.21.22.42.

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A segmentação é um procedimento importante na análise de imagens de sensoriamento remoto, que divide uma imagem em partes com propriedades uniformes e muda a menor unidade de uma imagem de pixel para segmento. Alguns fatores produzem resultados indesejados na segmentação de imagens de sensoriamento remoto e um desses fatores é a existência de sombras na cena. A iluminação afeta a segmentação de imagens porque as variações na iluminação da cena modificam os valores de pixel em todas as bandas espectrais. A concentração das informações de intensidade do sinal detectado em um canal minimiza os efeitos do brilho, sombras e degradês nos outros canais (i.e., matiz e saturação). A matiz está sendo usada para identificar objetos que se distinguem por esse atributo, e o mesmo princípio de detecção está sendo estendido para imagens de múltiplas bandas, embora não seja indicado em todos os casos. Para melhorar o processo é apresentado um modo de produzir um canal sintético de matiz fazendo-se a média de múltiplos canais de matizes ponderados pela saturação, correspondentes a bandas multiespectrais de uma imagem. A idéia central por trás dessa abordagem baseada em objetos é a capacidade de avaliar e alterar qualquer segmento antes da conclusão do processo de segmentação. Uma função de desvios (\textit{turning function}) é um modo de representação de polígonos pelos ângulos e comprimentos, que pode ser analisada e modificada conforme necessário. Cada segmento da imagem pertencente ao primeiro plano é submetido a análise e o segmento correspondente fica sujeito a alterações, quando necessário. Para se obter a função de desvios, primeiro o segmento é representado de modo binário (i.e., como um \textit{blob}) e, posteriormente, convertido para o código da cadeia de Freeman. O uso de índices é útil para categorizar formas e algumas métricas são apresentadas. Os algoritmos são implementados em linguagem IDL em dois modos: supervisionado e não-supervisionado. O primeiro consiste na indicação na imagem de uma pequena área do objeto. O segundo usa um algoritmo de crescimento de região com sementes aleatórias. Além da revisão do algoritmo original, ampliando suas capacidades, e a proposta de uma abordagem baseada em objeto para a técnica de segmentação esco-lhida, esta tese propõe um modo de fazer a segmentação em função do matiz extraído de imagens multiespectrais, utilizando um aplicativo de segmentação, e também cria uma banda sintética de matiz a partir das combinações das composições coloridas da imagem original.
Segmentation is an important procedure in remote sensing image analysis, which divides an image into parts with uniform properties and changes the smallest unit of an image from pixel to segmento Some factors produce undesired results in the segmentation of remote sensing imagery and one of such factors is due to illumination: the occurrence of shadows. Lighting affects image segmentation because variations on scene lighting modifies the pixel values in all spectral bands. The concentration of intensity on the sensed signal in one channel produces two other channels, hue and saturation, where the effects of glare, shadows and gradients are minimized. The hue is being used to identify objects that are distinguishable by this attribute, and the same principle of detection is being extended to multi-band images, but it is not suitable for all cases. To improve the process it is being presented a way to produce a saturation-weighted synthetic hue channel for multispectral imagery. The central idea behind this object-based approach is the ability to evaluate and change any segment before finishing the segmentation processo A turning function is a representation of polygons by angles and lengths and it may be analyzed and modified as necessary. Every segment identified as foreground undergoes to analysis and the corresponding segment is subject to changes. To obtain the turning function, the segment is first identified as a blob and then converted into a Freeman's chain co de formato The use of indexes is helpful to categorize shapes and some metrics are being presented. The algorithm is implemented in IDL language and it have two modes: unsupervised and supervised. The unsupervised uses a region growing algorithm with random seeds. The supervised consists on the manual indication of a small area of the object. Besides the enhancements on the algorithms and the proposal of a object-based approach for a chosen segmentation technique, this thesis also proposes a feasible way to make segmentation based on phase extracted from multispectral imagery using any segmentation software in addition to creating a synthetic hue band from the combinations of color compositions of the original imagery.

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Li, Yi. "Object and concept recognition for content-based image retrieval /". Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/7006.

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Stals, Jacobus Petrus. "Mapping potential soil salinization using rule based object-oriented image analysis". Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/2371.

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Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2007.
Soil salinization is a world wide environmental problem affecting plant growth and agricultural yields. Remote sensing has been used as a tool to detect and/or manage soil salinity. Object-oriented image analysis is a relatively new image analysis technique which allows analysis at different hierarchical scales, the use of relationships between objects and contextual information in the classification process, and the ability to create a rule based classification procedure. The Lower Orange River in South Africa is a region of successful irrigation farming along the river floodplain but also with the potential risk of soil salinization. This research attempted to detect and map areas of potential high soil salinity using digital aerial photography and digital elevation models. Image orthorectification was conducted on the digital aerial photographs. The radiometric variances between photographs made radiometric calibration of the photographs necessary. Radiometric calibration on the photographs was conducted using Landsat 7 satellite images as radiometric correction values, and image segmentation as the correction units for the photographs. After radiometric calibration, object-oriented analysis could be conducted on one analysis region and the developed rule bases applied to the other regions without the need for adjusting parameters. A rule based hierarchical classification was developed to detect vegetation stress from the photographs as well as salinity potential terrain features from the digital elevation models. These rule bases were applied to all analysis blocks. The detected potential high salinity indicators were analyzed spatially with field collected soil data in order to assess the capability of the classifications to detect actual salinization, as well as to assess which indicators were the best indicators of salinity potential. Vegetation stress was not a good indicator of salinity as many other indicators could also cause vegetation stress. Terrain indicators such as depressions in the landscape at a micro scale were the best indicators of potential soil salinization.

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Dulin, Mike W. "Identifying and assessing windbreaks in Ford County, Kansas using object-based image analysis". Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1517.

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Mühlfellner, Peter. "Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems". Thesis, Högskolan i Halmstad, Intelligenta system (IS-lab), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377.

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We propose a scalable and fexible hardware architecture for the extraction of image features, used in conjunction with an attentional cascade classifier for appearance-based object detection. Individual feature processors calculate feature-values in parallel, using parameter-sets and image data that is distributed via BRAM buffers. This approach can provide high utilization- and throughput-rates for a cascade classifier. Unlike previous hardware implementations, we are able to flexibly assign feature processors to either work on a single- or multiple image windows in parallel, depending on the complexity of the current cascade stage. The core of the architecture was implemented in the form of a streaming based FPGA design, and validated in simulation, synthesis, as well as via the use of a Logic Analyser for the verification of the on-chip functionality. For the given implementation, we focused on the design of Haar-like feature processors, but feature processors for a variety of heterogenous feature types, such as Gabor-like features, can also be accomodated by the proposed hardware architecture.

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Korzeniowska, Karolina [Verfasser] y Oliver [Akademischer Betreuer] Korup. "Object-based image analysis for detecting landforms diagnostic of natural hazards / Karolina Korzeniowska ; Betreuer: Oliver Korup". Potsdam : Universität Potsdam, 2017. http://d-nb.info/1218402792/34.

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Hulet, April. "An Object-Based Image Analysis of Treated and Untreated Pinyon and Juniper Woodlands Across the Great Basin". BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3406.

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Land managers need to rapidly assess vegetation composition and bare ground to effectively evaluate, manage, and restore shrub steppe communities that have been encroached by pinyon and juniper (P-J) trees. A major part of this process is assessing where to apply mechanical and prescribed fire treatments to reduce fuel loads and maintain or restore sagebrush steppe rangelands. Geospatial technologies, particularly remote sensing, offers an efficient option to assess rangelands across multiple spatial scales while reducing the need for ground-based sampling measurements. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for sagebrush steppe communities invaded by P-J trees at five sites in Oregon, California, Nevada, and Utah with a Vexcel Ultra CamX digital camera in June/July 2009. In addition to untreated P-J woodlands, imagery was acquired over P-J woodlands where fuels were reduced by either prescribed fire, tree cutting, or mastication treatments. Ground measurements were simultaneously collected at each site in 2009 on 0.1-hectare subplots as part of the Sagebrush Steppe Treatment Evaluation Project (SageSTEP). We used Trimble eCognition Developer to 1) develop efficient methods to estimate land cover classes found in P-J woodlands; 2) determine the relationship between ground measurements and object-based image analysis (OBIA) land cover measurements for the following classes: trees (live, burned, cut, and masticated), shrubs, perennial herbaceous vegetation, litter (including annual species), and bare ground; and 3) evaluate eCognition rule-sets (models) across four spatial scales (subplot, site, region, and network) using untreated P-J woodland imagery. At the site scale, the overall accuracy of our thematic maps for untreated P-J woodlands was 84% with a kappa statistic of 0.80. For treatments, the overall accuracy and kappa statistic for prescribed fire was 85% and 0.81; cut and fell 82% and 0.77, and mastication 84% and 0.80, respectively, each indicating strong agreement between OBIA classification and ground measured data. Differences between mean cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within 5% of each other; all regional and network OBIA mean cover estimates were within 10% of the ground measurements. The trade-off for decreased precision over a larger area (region and network scale) may be useful to prioritize fuel-management strategies but will unlikely capture subtle shifts in understory plant communities that site and subplot spatial scales often capture. Although cover assessments from OBIA differed somewhat from ground measurements, they were accurate enough for many landscape-assessment applications such as evaluating treatment success and assessing the spatial distribution of fuels following fuel-reduction treatments on a site scale.

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Nie, Qiong. "Cumulative methods for image based driver assistance systems : applications to egomotion estimation, motion analysis and object detection". Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112095/document.

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La thèse porte sur la détection d’objets à partir d’une caméra embarquée sur un véhicule mobile en exploitant l’approche monoculaire « c-vélocité ». Cette méthode s’inspire de la méthode appelée « v-disparité » utilisée en stéréovision : toutes deux ont pour objectif la détection d’objets en les approximant par des plans d’orientations différentes, ce qui permet d’éviter, en monoculaire, d’estimer la profondeur. Ces deux approches, monoculaires et binoculaires, permettent de transformer le problème complexe de la détection d’objets en un problème plus simple de détection de formes paramétriques simples (droites, paraboles) dans un nouvel espace de représentation où la détection peut être réalisée à l’aide d’une transformée de Hough. La « c-vélocité », pour être efficace, requiert un calcul assez précis du flot optique et une bonne estimation de la position du Foyer d’expansion (FOE). Dans cette thèse, nous avons étudié les approches existantes de calcul de flot optique et sommes arrivés à la conclusion qu’aucune n’est vraiment performante notamment sur les régions homogènes telle que la route dans les scènes qui correspondent à l’application que nous visons à savoir : les véhicules intelligents. Par ailleurs, les méthodes d’estimation du flot optique peinent également à fournir une bonne estimation dans le cas de déplacement importants dans les régions proches de la caméra. Nous proposons dans cette thèse d’exploiter à la fois un modèle 3D de la scène et une estimation approximative de la vitesse du véhicule à partir d’autres capteurs intégrés. L’utilisation de connaissances a priori permet de compenser le flot dominant pour faciliter l’estimation de la partie résiduelle par une approche classique. Par ailleurs, trois approches différentes sont proposées pour détecter le foyer d’expansion. Parmi elles, nous proposons une méthode novatrice permettant d’estimer le FOE en exploitant la norme du flot et la structure de la scène à partir d’un processus « c-vélocité » inversé. En plus d’améliorer ces étapes préliminaires, nous proposons aussi l’optimisation et l’accélération de l’algorithme « c-vélocité » par une implémentation multithread. Enfin, nous proposons une modification de l’approche c-vélocité d’origine afin d’anticiper une éventuelle coopération mouvement/stéréo, proposée en perspective, à travers un jumelage avec la v-disparité
This thesis is based on the detection of objects from an onboard moving camera by exploiting the monocular approach "c-velocity". This method is inspired by the method called "v-disparity" used in stereovision: both methods aim at detecting objects by approximating objects into plans with different orientations. Such approximation can avoid to estimate the depth in monocularvision. These two approaches, monocular and binocular, allow to transform the complex objet détection problem into a more simple parametric forms (eg. lines) detection in a new space, where these formes can be easily extracted using Hough Transform.The “c-velocity”, to make it effective, requires an accurate computation of optical flow and a good estimation of the focus of expansion (FOE) location. Therefore, we have studied the existing approaches of optical flow estimation and arrived at the conclusion that none of them is really powerful especially on the homogeneous regions such as road surface. In addition, the optical flow estimation methods also struggle to provide a good estimate in the case of huge displacement in the areas close to the camera. We propose in this thesis to exploit both a 3D model of the scene and a rough estimate about the vehicle speed from other integrated sensors. Using a priori knowledge allows to compensate the dominant optical flow and to facilitate the estimation of the rest part by a classical approach. In addition, three different approaches are proposed to detect the focus of expansion. Among them, we propose a novel method for estimating FOE by leveraging the flow norm and the scene structure from an inverse “c-velocity“ process. In addition to improve these preliminary steps, we also propose an acceleration and optimization of the “c-velocity“ algorithm by a multi-thread implementation. Finally, we propose a modification to the original “c-velocity“ approach in order to anticipate a possible cooperation motion/stereo, proposed in perspective, with the “v-disparity“ approach

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17

Kutz, Kain Markus. "Inclusion of Gabor textural transformations and hierarchical structures within an object based analysis of a riparian landscape". Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6167.

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Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances are unable to utilize the amount of information encompassed within ultra-high (sub-meter) resolution imagery. Previously, users have typically reduced the resolution of imagery in an attempt to more closely represent the interpretation or object scale in an image and rid the image of any extraneous information within the image that may cause the OBIA process to identify too small of objects when performing semi-automated delineation of objects based on an images’ properties (Mas et al., 2015; Eiesank et al., 2014; Hu et al., 2010). There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape.

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18

Viksten, Fredrik. "Local Features for Range and Vision-Based Robotic Automation". Doctoral thesis, Linköpings universitet, Informationskodning, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57333.

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Robotic automation has been a part of state-of-the-art manufacturing for many decades. Robotic manipulators are used for such tasks as welding, painting, pick and place tasks etc. Robotic manipulators are quite flexible and adaptable to new tasks, but a typical robot-based production cell requires extensive specification of the robot motion and construction of tools and fixtures for material handling. This incurs a large effort both in time and monetary expenses. The task of a vision system in this setting is to simplify the control and guidance of the robot and to reduce the need for supporting material handling machinery. This dissertation examines performance and properties of the current state-of-the-art local features within the setting of object pose estimation. This is done through an extensive set of experiments replicating various potential problems to which a vision system in a robotic cell could be subjected. The dissertation presents new local features which are shown to increase the performance of object pose estimation. A new local descriptor details how to use log-polar sampled image patches for truly rotational invariant matching. This representation is also extended to use a scale-space interest point detector which in turn makes it very competitive in our experiments. A number of variations of already available descriptors are constructed resulting in new and competitive features, among them a scale-space based Patch-duplet. In this dissertation a successful vision-based object pose estimation system is extended for multi-cue integration, yielding increased robustness and accuracy. Robustness is increased through algorithmic multi-cue integration, combining the individual strengths of multiple local features. Increased accuracy is achieved by utilizing manipulator movement and applying temporal multi-cue integration. This is implemented using a real flexible robotic manipulator arm. Besides work done on local features for ordinary image data a number of local features for range data has also been developed. This dissertation describes the theory behind and the application of the scene tensor to the problem of object pose estimation. The scene tensor is a fourth order tensor representation using projective geometry. It is shown how to use the scene tensor as a detector as well as how to apply it to the task of object pose estimation. The object pose estimation system is extended to work with 3D data. A novel way of handling sampling of range data when constructing a detector is discussed. A volume rasterization method is presented and the classic Harris detector is adapted to it. Finally, a novel region detector, called Maximally Robust Range Regions, is presented. All developed detectors are compared in a detector repeatability test.

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19

Fourie, Christoffel Ettienne [Verfasser], Sören [Akademischer Betreuer] Hese y Feitosa [Akademischer Betreuer] Raul. "Sample supervised search centric approaches in geographic object-based image analysis / Christoffel Ettienne Fourie. Gutachter: Sören Hese ; Feitosa Raul". Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2015. http://d-nb.info/1080521976/34.

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20

Fourie, Christoffel [Verfasser], Sören [Akademischer Betreuer] Hese y Feitosa [Akademischer Betreuer] Raul. "Sample supervised search centric approaches in geographic object-based image analysis / Christoffel Ettienne Fourie. Gutachter: Sören Hese ; Feitosa Raul". Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2015. http://d-nb.info/1080521976/34.

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21

Slobodan, Dražić. "Shape Based Methods for Quantification and Comparison of Object Properties from Their Digital Image Representations". Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=107871&source=NDLTD&language=en.

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The thesis investigates development, improvement and evaluation of methods for quantitative characterization of objects from their digital images and similarity measurements between digital images. Methods for quantitative characterization of objects from their digital images are increasingly used in applications in which error can have crtical consequences, but the traditional methods for shape quantification are of low precision and accuracy. In the thesis is shown that the coverage of a pixel by a shape can be used to highly improve the accuracy and precision of using digital images to estimate the maximal distance between objects furthest points measured in a given direction. It is highly desirable that a distance measure between digital images can be related to a certain shape property and morphological operations are used when defining a distance for this purpose. Still, the distances defined in this manner turns out to be insufficiently sensitive to relevant data representing shape properties in images. We show that the idea of adaptive mathematical morphology can be used successfully to overcome problems related to sensitivity of distances defined via morphological operations when comparing objects from their digital image representations.
У тези су размотрени развој, побољшање и евалуација метода за квантитативну карактеризацију објеката приказаних дигиталним сликама, као и мере растојања између дигиталних слика. Методе за квантитативну карактеризацију објеката представљених дигиталним сликама се све више користе у применама у којима грешка може имати критичне последице, а традиционалне методе за квантитативну карактеризацију су мале прецизности и тачности. У тези се показује да се коришћењем информације о покривеност пиксела обликом може значајно побољшати прецизност и тачност оцене растојања између две најудаљеније тачке облика мерено у датом правцу. Веома је пожељно да мера растојања између дигиталних слика може да се веже за одређену особину облика и морфолошке операције се користе приликом дефинисања растојања у ту сврху. Ипак, растојања дефинисана на овај начин показују се недовољно осетљива на релевантне податке дигиталних слика који представљају особине облика. У тези се показује да идеја адаптивне математичке морфологије може успешно да се користи да би се превазишао поменути проблем осетљивости растојања дефинисаних користећи морфолошке операције.
U tezi su razmotreni razvoj, poboljšanje i evaluacija metoda za kvantitativnu karakterizaciju objekata prikazanih digitalnim slikama, kao i mere rastojanja između digitalnih slika. Metode za kvantitativnu karakterizaciju objekata predstavljenih digitalnim slikama se sve više koriste u primenama u kojima greška može imati kritične posledice, a tradicionalne metode za kvantitativnu karakterizaciju su male preciznosti i tačnosti. U tezi se pokazuje da se korišćenjem informacije o pokrivenost piksela oblikom može značajno poboljšati preciznost i tačnost ocene rastojanja između dve najudaljenije tačke oblika mereno u datom pravcu. Veoma je poželjno da mera rastojanja između digitalnih slika može da se veže za određenu osobinu oblika i morfološke operacije se koriste prilikom definisanja rastojanja u tu svrhu. Ipak, rastojanja definisana na ovaj način pokazuju se nedovoljno osetljiva na relevantne podatke digitalnih slika koji predstavljaju osobine oblika. U tezi se pokazuje da ideja adaptivne matematičke morfologije može uspešno da se koristi da bi se prevazišao pomenuti problem osetljivosti rastojanja definisanih koristeći morfološke operacije.

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22

Mahmoud, El-Abbas Mustafa Mustafa. "Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach". Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-161511.

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Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information. An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes. Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA. Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high. Aggregated to the community-level, social survey of household data provides a comprehensive perspective additionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception. At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attribute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used affect the quality of the developed model this was also discussed based on the different sensors examined. To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces
Da das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten. Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverarbeitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen. Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der Pixel-basierten Konzept verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert. Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post-Klassifizierungs- Änderungserkennung besser geeignet ist als radiometrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen. Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semi-automatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die besten Modelle waren mit einem unabhängigen Satz von kreuz-validierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen. Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln

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23

Hast, Isak y Asmelash Mehari. "Automating Geographic Object-Based Image Analysis and Assessing the Methods Transferability : A Case Study Using High Resolution Geografiska SverigedataTM Orthophotos". Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-22570.

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Geographic object-based image analysis (GEOBIA) is an innovative image classification technique that treats spatial features in an image as objects, rather than as pixels; thus resembling closer to that of human perception of the geographic space. However, the process of a GEOBIA application allows for multiple interpretations. Particularly sensitive parts of the process include image segmentation and training data selection. The multiresolution segmentation algorithm (MSA) is commonly applied. The performance of segmentation depends primarily on the algorithms scale parameter, since scale controls the size of image objects produced. The fact that the scale parameter is unit less makes it a challenge to select a suitable one; thus, leaving the analyst to a method of trial and error. This can lead to a possible bias. Additionally, part from the segmentation, training area selection usually means that the data has to be manually collected. This is not only time consuming but also prone to subjectivity. In order to overcome these challenges, we tested a GEOBIA scheme that involved automatic methods of MSA scale parameterisation and training area selection which enabled us to more objectively classify images. Three study areas within Sweden were selected. The data used was high resolution Geografiska Sverigedata (GSD) orthophotos from the Swedish mapping agency, Lantmäteriet. We objectively found scale for each classification using a previously published technique embedded as a tool in eCognition software. Based on the orthophoto inputs, the tool calculated local variance and rate of change at different scales. These figures helped us to determine scale value for the MSA segmentation. Moreover, we developed in this study a novel method for automatic training area selection. The method is based on thresholded feature statistics layers computed from the orthophoto band derivatives. Thresholds were detected by Otsu’s single and multilevel algorithms. The layers were run through a filtering process which left only those fit for use in the classification process. We also tested the transferability of classification rule-sets for two of the study areas. This test helped us to investigate the degree to which automation can be realised. In this study we have made progress toward a more objective way of object-based image classification, realised by automating the scheme. Particularly noteworthy is the algorithm for automatic training area selection proposed, which compared to manual selection restricts human intervention to a minimum. Results of the classification show overall well delineated classes, in particular, the border between open area and forest contributed by the elevation data. On the other hand, there still persists some challenges regarding separating between deciduous and coniferous forest. Furthermore, although water was accurately classified in most instances, in one of the study areas, the water class showed contradictory results between its thematic and positional accuracy; hence stressing the importance of assessing the result based on more than the thematic accuracy. From the transferability test we noted the importance of considering the spatial/spectral characteristics of an area before transferring of rule-sets as these factors are a key to determine whether a transfer is possible.

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24

Johansen, Richard A. "An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis". University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1429280225.

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25

Palm, Fredrik. "Urban Vegetation Mapping Using Remote Sensing Techniques : A Comparison of Methods". Thesis, Stockholms universitet, Institutionen för naturgeografi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-117108.

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The aim of this study is to compare remote sensing methods in the context of a vegetation mapping of an urban environment. The methods used was (1) a traditional per-pixel based method; maximum likelihood supervised classification (ENVI), (2) a standard object based method; example based feature extraction (ENVI) and (3) a newly developed method; Window Independent Contextual Segmentation (WICS) (Choros Cognition). A four-band SPOT5 image with a pixel size of 10x10m was used for the classifications. A validation data-set was created using a ortho corrected aerial image with a pixel size of 1x1m. Error matrices was created by cross-tabulating the classified images with the validation data-set. From the error matrices, overall accuracy and kappa coefficient was calculated. The object-based method performed best with a overall accuracy of 80% and a kappa value of 0.6, followed by the WICS method with an overall accuracy of 77% and a kappa value of 0.53, placing the supervised classification last with an overall accuracy of 71% and a kappa value of 0.38. The results of this study suggests object-based method and WICS to perform better than the supervised classification in an urban environment.

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26

Dey, Vivek. "A Supervised Approach For The Estimation Of Parameters Of Multiresolution Segementation And Its Application In Building Feature Extraction From VHR Imagery". Thesis, Fredericton: University of New Brunswick, 2011. http://hdl.handle.net/1882/35388.

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With the advent of very high spatial resolution (VHR) satellite, spatial details within the image scene have increased considerably. This led to the development of object-based image analysis (OBIA) for the analysis of VHR satellite images. Image segmentation is the fundamental step for OBIA. However, a large number of techniques exist for RS image segmentation. To identify the best ones for VHR imagery, a comprehensive literature review on image segmentation is performed. Based on that review, it is found that the multiresolution segmentation, as implemented in the commercial software eCognition, is the most widely-used technique and has been successfully applied for wide variety of VHR images. However, the multiresolution segmentation suffers from the parameter estimation problem. Therefore, this study proposes a solution to the problem of the parameter estimation for improving its efficiency in VHR image segmentation. The solution aims to identify the optimal parameters, which correspond to optimal segmentation. The solution to the parameter estimation is drawn from the Equations related to the merging of any two adjacent objects in multiresolution segmentation. The solution utilizes spectral, shape, size, and neighbourhood relationships for a supervised solution. In order to justify the results of the solution, a global segmentation accuracy evaluation technique is also proposed. The solution performs excellently with the VHR images of different sensors, scenes, and land cover classes. In order to justify the applicability of solution to a real life problem, a building detection application based on multiresolution segmentation from the estimated parameters, is carried out. The accuracy of the building detection is found nearly to be eighty percent. Finally, it can be concluded that the proposed solution is fast, easy to implement and effective for the intended applications.

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Schwier, Michael [Verfasser], Horst Karl [Akademischer Betreuer] [Gutachter] Hahn, Herbert [Gutachter] Jaeger y Gitta [Gutachter] Domik-Kienegger. "Object-based Image Analysis for Detection and Segmentation Tasks in Biomedical Imaging / Michael Schwier. Betreuer: Horst Karl Hahn. Gutachter: Horst Karl Hahn ; Herbert Jaeger ; Gitta Domik-Kienegger". Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2016. http://d-nb.info/1111884501/34.

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Myburgh, Gerhard. "The impact of training set size and feature dimensionality on supervised object-based classification : a comparison of three classifiers". Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71655.

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Thesis (MSc)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Supervised classifiers are commonly used in remote sensing to extract land cover information. They are, however, limited in their ability to cost-effectively produce sufficiently accurate land cover maps. Various factors affect the accuracy of supervised classifiers. Notably, the number of available training samples is known to significantly influence classifier performance and to obtain a sufficient number of samples is not always practical. The support vector machine (SVM) does perform well with a limited number of training samples. But little research has been done to evaluate SVM’s performance for geographical object-based image analysis (GEOBIA). GEOBIA also allows the easy integration of additional features into the classification process, a factor which may significantly influence classification accuracies. As such, two experiments were developed and implemented in this research. The first compared the performances of object-based SVM, maximum likelihood (ML) and nearest neighbour (NN) classifiers using varying training set sizes. The effect of feature dimensionality on classifier accuracy was investigated in the second experiment. A SPOT 5 subscene and a four-class classification scheme were used. For the first experiment, training set sizes ranging from 4-20 per land cover class were tested. The performance of all the classifiers improved significantly as the training set size was increased. The ML classifier performed poorly when few (<10 per class) training samples were used and the NN classifier performed poorly compared to SVM throughout the experiment. SVM was the superior classifier for all training set sizes although ML achieved competitive results for sets of 12 or more training samples per class. Training sets were kept constant (20 and 10 samples per class) for the second experiment while an increasing number of features (1 to 22) were included. SVM consistently produced superior classification results. SVM and NN were not significantly (negatively) affected by an increase in feature dimensionality, but ML’s ability to perform under conditions of large feature dimensionalities and few training areas was limited. Further investigations using a variety of imagery types, classification schemes and additional features; finding optimal combinations of training set size and number of features; and determining the effect of specific features should prove valuable in developing more costeffective ways to process large volumes of satellite imagery. KEYWORDS Supervised classification, land cover, support vector machine, nearest neighbour classification maximum likelihood classification, geographic object-based image analysis
AFRIKAANSE OPSOMMING: Gerigte klassifiseerders word gereeld aangewend in afstandswaarneming om inligting oor landdekking te onttrek. Sulke klassifiseerders het egter beperkte vermoëns om akkurate landdekkingskaarte koste-effektief te produseer. Verskeie faktore het ŉ uitwerking op die akkuraatheid van gerigte klassifiseerders. Dit is veral bekend dat die getal beskikbare opleidingseenhede ŉ beduidende invloed op klassifiseerderakkuraatheid het en dit is nie altyd prakties om voldoende getalle te bekom nie. Die steunvektormasjien (SVM) werk goed met beperkte getalle opleidingseenhede. Min navorsing is egter gedoen om SVM se verrigting vir geografiese objek-gebaseerde beeldanalise (GEOBIA) te evalueer. GEOBIA vergemaklik die integrasie van addisionele kenmerke in die klassifikasie proses, ŉ faktor wat klassifikasie akkuraathede aansienlik kan beïnvloed. Twee eksperimente is gevolglik ontwikkel en geïmplementeer in hierdie navorsing. Die eerste eksperiment het objekgebaseerde SVM, maksimum waarskynlikheids- (ML) en naaste naburige (NN) klassifiseerders se verrigtings met verskillende groottes van opleidingstelle vergelyk. Die effek van kenmerkdimensionaliteit is in die tweede eksperiment ondersoek. ŉ SPOT 5 subbeeld en ŉ vier-klas klassifikasieskema is aangewend. Opleidingstelgroottes van 4-20 per landdekkingsklas is in die eerste eksperiment getoets. Die verrigting van die klassifiseerders het beduidend met ŉ toename in die grootte van die opleidingstelle verbeter. ML het swak presteer wanneer min (<10 per klas) opleidingseenhede gebruik is en NN het, in vergelyking met SVM, deurgaans swak presteer. SVM het die beste presteer vir alle groottes van opleidingstelle alhoewel ML kompeterend was vir stelle van 12 of meer opleidingseenhede per klas. Die grootte van die opleidingstelle is konstant gehou (20 en 10 eenhede per klas) in die tweede eksperiment waarin ŉ toenemende getal kenmerke (1 tot 22) toegevoeg is. SVM het deurgaans beter klassifikasieresultate gelewer. SVM en NN was nie beduidend (negatief) beïnvloed deur ŉ toename in kenmerkdimensionaliteit nie, maar ML se vermoë om te presteer onder toestande van groot kenmerkdimensionaliteite en min opleidingsareas was beperk. Verdere ondersoeke met ŉ verskeidenheid beelde, klassifikasie skemas en addisionele kenmerke; die vind van optimale kombinasies van opleidingstelgrootte en getal kenmerke; en die bepaling van die effek van spesifieke kenmerke sal waardevol wees in die ontwikkelling van meer koste effektiewe metodes om groot volumes satellietbeelde te prosesseer. TREFWOORDE Gerigte klassifikasie, landdekking, steunvektormasjien, naaste naburige klassifikasie, maksimum waarskynlikheidsklassifikasie, geografiese objekgebaseerde beeldanalise

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Stammler, Melanie. "Investigating Sand Dune Location, Orientation and Geomorphometry Through GEOBIA-Based Mapping: A Case Study in Northern Sweden". Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424975.

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Climate change has repeatedly been framed as the defining issue of the Anthropocene and with the Arctic changing at unpreceded speed need is high for a profound understanding of the Northern Swedish landscape. Northern Swedish aeolian sand dunes have been impacted by climatic changes throughout time. Their location, orientation and geomorphometry can therefore be used to explore past wind patterns and dune activity. By systematically and spatially mapping the dunes, patterns in location can be illustrated, dune orientations investigated, the dunes’ geomorphometry characterised and sediment sources determined. Based on this knowledge, insight in landscape development along with a better understanding of long-term landscape (in)stability in Northern Sweden can be gained. This M. Sc. thesis sets out to summarize useful concepts to understand the formation of Northern Swedish aeolian sand dunes and to derive its implications for understanding landscape development. Based thereon, it deduces the strong need to systematically and spatially analyse aeolian sand dunes in Northern Sweden. The use of geographic object-based image analysis (GEOBIA) allows for the detection of potential dune locations over a large area and provides defined and reproducible mapping boundaries. Polygons are created by segmenting a residual-relief separated digital elevation model (DEM) as well as slope and curvature data. The multi-resolution segmentation provides best results with a scale parameter of 15 and a homogeneity criterion of 0.1 for the shape criterion, as well as 0.5 for the compactness criterion. A rule-based classification with empirically derived parameters accepts on average 2.5 % of the segmented image objects as potential dune sites. Subsequent expert-decision confirms on average 25 % of the classified image objects as identified dune locations. The rule-based classification provides best results when targeting a smaller area as this allows for less variability within the dune characteristics. The investigation of expert-accepted dune locations confirms a prevalence of parabolic dune forms, reveals the coexistence of simple dunes with large coalesced systems, exemplifies variation in dune orientation and highlights that the majority of dunes are supplied by glaciofluvial deposits. By mapping Northern Swedish aeolian sand dunes and investigating their meaning for landscape development, this thesis furthermore contributes to closing the gap identified for research on Northern Swedish aeolian sand dunes.
Den första associationen till sanddyner är säkert Sahara snarare än norra Sverige. Ändå är dessa fossila sanddyner också mycket relevanta och intressanta att studera. De kan analyseras i samband med det omgivande landskapet och dess orientering. Dessa egenskaper hjälper till att identifiera mönster i landskapsutveckling. Detta och på grund av dynarnas relativt gamla ålder kan slutsatser om landskapets (in)stabilitet på geologiska tidsskalor dras. Detta är mycket användbart eftersom det kan ge insikter om hur klimatet såg ut under tiden som sanddynerna bildades - perioder där människor ännu inte har bevittnat klimatet. Kunskap som till exempel hur klimatet som rådde för länge sedan såg ut kan användas bland annat för att uppskatta hur landskapet kommer förändras i framtiden till följd av klimatförändringar. Trots dessa användbara egenskaper hos sanddynerna har lite forskning gjorts hittills. Det här examensarbet försöker motverka detta kunskapsgap och kartlägger sanddyner i norra Sverige med hjälp av geografisk objektbaserad bildanalys (geographic object-based image analysis, GEOBIA). Det innebär att bildmaterial och digitala höjdmodeller frigjorda från vegetation automatiskt analyseras med hjälp av algoritmer. Fokus här är inte på att analysera enskilda pixlar. Snarare grupperas pixlar med liknande egenskaper så som lutning (slope), krökning (curvature) och spektralegenskaper. Dessa blir sedan grunden för analysen. Möjliga sanddyner upptäcks semi-automatiskt så att deras position och orientering sedan kan analyseras. Den kunskap som erhållits på detta sätt utgör grunden för vidare forskning. Ett annat mål är att bidra till en djupare förståelse kring landskapsutvecklingen i norra Sverige. Det är viktigt att komma ihåg att detta är ett område som särskilt påverkas av klimatförändringar. En ökad kunskap om landskapets tidigare klimatrespons kan därmed bidra till att förutsäga framtiden för denna region. Förutom att öka kunskapen kring sanddyner i norra Sverige hjälper det här mastersarbetet även till att utvidga användningen av GEOBIA inom geomorfologiska studier.

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30

Ruiz, Luis Fernando Chimelo. "Uma abordagem de classificação da cobertura da terra em imagens obtidas por veículo aéreo não tripulado". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/111857.

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Câmaras não métricas acopladas a Veículos Aéreos Não Tripulados (VANT) possibilitam coleta de imagens com alta resolução espacial e temporal. Além disso, o custo de operação e manutenção desses equipamentos são reduzidos. A classificação da cobertura da terra por meio dessas imagens são dificultadas devido à alta variabilidade espectral dos alvos e ao grande volume de dados gerados. Esses contratempos são contornados utilizando Análise de Imagens Baseada em Objetos (Object-Based Image Analysis – OBIA) e algoritmos de mineração de dados. Um algoritmo empregado na OBIA são as Árvores de Decisão (AD). Essa técnica possibilita tanto a seleção de atributos mais informativos quanto a classificação das regiões. Novas técnicas de AD foram desenvolvidas e, nessas inovações, foram inseridas funções para selecionar atributos e para melhorar a classificação. Um exemplo é o algoritmo C5.0, que possui uma função de redução de dados e uma de reforço. Nesse contexto, este trabalho tem como objetivo (i) avaliar o método de segmentação por crescimento de regiões em imagens com altíssima resolução espacial, (ii) determinar os atributos preditivos mais importantes na discriminação das classes e (iii) avaliar as classificações das regiões em relação aos parâmetros de seleção dos atributos (winnow) e de reforço (trial), que estão contidos no algoritmo C5.0. A segmentação da imagem foi efetuada no programa Spring, já as regiões geradas na segmentação foram classificadas pelo modelo de AD C5.0, que está disponível no programa R. Como resultado foi identificado que a segmentação crescimento de regiões possibilitou uma alta correspondência com regiões geradas pelo especialista, resultando em valores de Reference Bounded Segments Booster (RBSB) próximos a 0. Os atributos mais importantes na construção dos modelos por AD foram a razão entre a banda do verde com a azul (r_v_a) e o Modelo Digital de Elevação (MDE). Para o parâmetro de reforço (trial), não foi identificada melhora na acurácia da classificação ao aumentar seu valor. Já o parâmetro winnow possibilitou uma redução no número de atributos preditivos, sem perdas estatisticamente significativas na acurácia da classificação. A função de reforço (trial) não melhorou a classificação da cobertura da terra. Também não foram constatadas diferenças estatisticamente significativas quando winnow selecionado como verdadeiro, mas se encontrou o benefício desse último parâmetro reduzindo a dimensionalidade dos dados. Nesse sentido, este trabalho contribuiu para a classificação da cobertura da terra em imagens coletadas por VANT, uma vez que se desenvolveu algoritmos para automatizar os processos da OBIA e para avaliar a classificação das regiões em relação às funções de reforço (winnow) e de seleção do atributo (winnow) do classificador por árvore de decisão C5.0.
Non-metric cameras attached to Unmanned Aerial Vehicles (UAV) enable collection of images with high spatial and temporal resolution. In addition, the cost of operation and maintenance of equipment are reduced. The land cover classification through these images are hampered due to high spectral variability of the targets and the large volume of data generated. These setbacks are contoured using Image Analysis Based on Objects (OBIA) and data mining algorithms. An algorithm used in OBIA are Decision Trees (AD). This technique allows the selection of the most informative attributes as the classification of regions. New AD techniques have been developed and these innovations, were functions inserted to select attributes and to improve classification. One example is a C5.0 algorithm, which has a data reduction function and of boosting. In this context, this paper aims to (i) evaluate the segmentation method for growing regions in images with high spatial resolution, (ii) determine the most important predictive attributes in the discrimination of classes and (iii) evaluate the classifications of regions regarding the attributes selection parameters (winnow) and boosting (trial), which are contained in the C5.0 algorithm. The image segmentation was performed in Spring program, since the regions generated in segmentation were classified by model C5.0 , which is available in the program R. As a result it was identified that the segmentation by region growing provided a high correlation with regions generated by the expert, resulting in Reference Bounded Segments Booster values (RBSB) near 0. The most important features in the construction of models of decision tree are the ratio between the band of green with the blue (r_v_a) and the Digital Elevation Model (DEM). Was not identified improvement in classification accuracy when was increased value of trial parameter. Already winnow parameter enabled a reduction in the number of predictive attributes, with no statistically significant losses in the accuracy of the classification. The boosting function (trial) did not improve the classification of land cover. Also were not found statistically significant differences when winnow selected as true, but was found the benefit of the latter parameter to reducing the dimensionality of the data. Thus, this work contributed to the land cover classification in images collected by UAV, once that were developed algorithms to automate the processes of integration OBIA and decision tree (C5.0).

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Nasonova, Sasha. "Estimating Arctic sea ice melt pond fraction and assessing ice type separability during advanced melt". Thesis, Remote Sensing, 2017. https://dspace.library.uvic.ca//handle/1828/9313.

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Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to discriminate between major ice types during winter and advanced melt, with a focus on advanced melt. RCM parameters with highest discrimination ability in conjunction with optimal GLCM texture features were used as input parameters for Support Vector Machine (SVM) supervised classifications. The results indicate that steep incidence angle RCM parameters show promise for distinguishing between FYI and MYI during advanced melt with an overall classification accuracy of 77.06%. The addition of GLCM texture parameters improved accuracy to 85.91%. This thesis provides valuable contributions to the growing body of literature on fp parameterization and SAR ice type discrimination during advanced melt.
Graduate
2019-03-21

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Roundy, Darrell B. "Estimating Pinyon and Juniper Cover Across Utah Using NAIP Imagery". BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5575.

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Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature AnalystTM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point) on 309 subplots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was > 45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point method (r = 0.85 for ENVI, 0.83 for Feature Analyst, and 0.83 for eCognition). Results from this study suggest that OBIA techniques may be used to extract P-J tree canopy cover accurately and inexpensively. All software packages accurately evaluated accurately extracted P-J canopy cover from NAIP imagery when imagery was not blurred and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel's oak), which are shrubs with similar spectral values as P-J.

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Salim, Aline. "Caracterização do uso da terra em periferias urbanas utilizando geotecnologias: bacia do Reservatório Guarapiranga". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/8/8135/tde-05112013-105350/.

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O estudo das cidades requer um olhar amplo, capaz de identificar e relacionar os inúmeros processos que atuam na produção do espaço urbano. Geotecnologias comumente são utilizadas para adquirir informação detalhada da cobertura da terra do espaço urbano. Neste contexto, o objetivo desta pesquisa é a proposição de metodologia para geração de informações da ocupação urbana nas periferias, definindo procedimentos para análise urbana que gere informações sobre as características da ocupação urbana, a partir de imagens de satélite de alta resolução espacial. Para tanto, foi escolhida como área de estudo o distrito do Jardim São Luís compreendido na bacia do reservatório Guarapiranga, manancial que fornece água para a Região Metropolitana de São Paulo (RMSP) e cuja bacia é área de proteção e recuperação de mananciais, de acordo com a legislação estadual. Foram realizadas discussões de como se organiza o espaço urbano e dos processos que refletiram na ocupação urbana da periferia da RMSP. A metodologia desenvolvida nesta pesquisa articulou o uso de técnicas de Sensoriamento Remoto e Sistemas de Informação Geográfica com dados socioeconômicos do censo demográfico. Os resultados foram apresentados e discutidos e a metodologia proposta demonstrou-se promissora para ser aplicada na atualização de informação do espaço urbano para subsidiar o planejamento urbano e a gestão territorial e consequentemente, para a melhoria da qualidade de vida da população.
Studies from cities require a wide look to identify the amount of processes occurring in the production of the urban space. Geo-technologies are commonly used to acquire detailed information of land cover from the urban space. In this context, the objective of this study is to propose methodology for the generation of information from the occupation at the urban peripheries, defining procedures for the analysis of urban areas, to obtain information of the characteristics of this occupation, from high resolution satellite images. The area under study was the district Jardim São Luis, located at the Guarapiranga Reservoir basin, an important water supplier for the São Paulo Metropolitan Region (RMSP), an area of environmental protection and recuperation, according to State legislation. Discussions were made on how the urban space is organized and on the processes of urban occupation in the periphery of RMSP. The methodology developed in this study used remote sensing and GIS techniques and socio-economic data from the last demographic census. The results were presented and the methodology proposed is very promising to be used to update information of the urban space and land management and consequently to improve the quality of life from the population.

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Mahmoud, El-Abbas Mustafa Mustafa [Verfasser], Elmar [Akademischer Betreuer] Csaplovics, Elsiddig Elnour [Akademischer Betreuer] Abdalla y Hannelore [Akademischer Betreuer] Kusserow. "Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach / Mustafa Mahmoud El-Abbas Mustafa. Gutachter: Elmar Csaplovics ; Elnour Abdalla Elsiddig ; Hannelore Kusserow. Betreuer: Elmar Csaplovics". Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://d-nb.info/1069096466/34.

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Leão, Junior Emerson [UNESP]. "Análise da qualidade da informação produzida por classificação baseada em orientação a objeto e SVM visando a estimativa do volume do reservatório Jaguari-Jacareí". Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152234.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Considerando o cenário durante a crise hídrica de 2014 e a situação crítica dos reservatórios do sistema Cantareira no estado de São Paulo, este estudo realizado no reservatório Jaguari-Jacareí, consistiu na extração de informações a partir de imagens multiespectrais e análise da qualidade da informação relacionada com a acurácia no cálculo do volume de água do reservatório. Inicialmente, a superfície do espelho d’água foi obtida pela classificação da cobertura da terra a partir de imagens multiespectrais RapidEye tomadas antes e durante a crise hídrica (2013 e 2014, respectivamente), utilizando duas abordagens distintas: classificação orientada a objeto (Object-based Image Analysis - OBIA) e classificação baseada em pixel (Support Vector Machine – SVM). A acurácia do usuário por classe permitiu expressar o erro para detectar a superfície do espelho d’água para cada abordagem de classificação de 2013 e 2014. O segundo componente da estimação do volume foi a representação do relevo submerso, que considerou duas fontes de dados na construção do modelo numérico do terreno (MNT): dados topográficos provenientes de levantamento batimétrico disponibilizado pela Sabesp e o modelo de superfície AW3D30 (ALOS World 3D 30m mesh), para complementar a informação não disponível além da cota 830,13 metros. A comparação entre as duas abordagens de classificação dos tipos de cobertura da terra do entorno do reservatório Jaguari-Jacareí mostrou que SVM resultou em indicadores de acurácia ligeiramente superiores à OBIA, para os anos de 2013 e 2014. Em relação à estimação de volume do reservatório, incorporando a informação do nível de água divulgado pela Sabesp, a abordagem SVM apresentou menor discrepância relativa do que OBIA. Apesar disso, a qualidade da informação produzida na estimação de volume, resultante da propagação da variância associada aos dados envolvidos no processo, ambas as abordagens produziram valores similares de incerteza, mas com uma sutil superioridade de OBIA, para alguns dos cenários avaliados. No geral, os métodos de classificação utilizados nesta dissertação produziram informação acurada e adequada para o monitoramento de recursos hídricos e indicou que a abordagem SVM teve um desempenho sutilmente superior na classificação dos tipos de cobertura da terra, na estimação do volume e em alguns dos cenários considerados na propagação da incerteza.
This study aims to extract information from multispectral images and to analyse the information quality in the water volume estimation of Jaguari-Jacareí reservoir. The presented study of changes in the volume of the Jaguari-Jacareí reservoir was motivated by the critical situation of the reservoirs from Cantareira System in São Paulo State caused by water crisis in 2014. Reservoir area was extracted from RapidEye multispectral images acquired before and during the water crisis (2013 and 2014, respectively) through land cover classification. Firstly, the image classification was carried out in two distinct approaches: object-based (Object-based Image Analysis - OBIA) and pixel-based (Support Vector Machine - SVM) method. The classifications quality was evaluated through thematic accuracy, in which for every technique the user accuracy allowed to express the error for the class representing the water in 2013 and 2014. Secondly, we estimated the volume of the reservoir’s water body, using the numerical terrain model generated from two additional data sources: topographic data from a bathymetric survey, available from Sabesp, and the elevation model AW3D30 (to complement the information in the area where data from Sabesp was not available). When compare the two classification techniques, it was found that in the image classification, SVM performance slightly overcame the OBIA classification technique for 2013 and 2014. In the volume calculation considering the water level estimated from the generated DTM, the result obtained by SVM approach was better in 2013, whereas OBIA approach was more accurate in 2014. Considering the quality of the information produced in the volume estimation, both approaches presented similar values of uncertainty, with the OBIA method slightly less uncertain than SVM. In conclusion, the classification methods used in this dissertation produced accurate information to monitor water resource, but SVM had a subtly superior performance in the classification of land cover types, volume estimation and some of the scenarios considered in the propagation of uncertainty.

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Menezes, Diego Pinheiro de. "Regeneração florestal após desmatamento: estudo da região de Santarém, Pará, Brasil". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/100/100136/tde-15052017-220805/.

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A superfície da terra foi modificada nos últimos 50 anos mais do que em qualquer outro período da História, mais intensa e rápida nos trópicos pela expansão das frentes de ocupação humana sobre floresta madura. A Amazônia brasileira, caracterizada pela alternância de ciclos econômicos extrativistas, exemplifica esse processo. Entre o abandono de áreas degradadas e a abertura de novas frentes de ocupação, ocorre a regeneração florestal. A floresta secundária tem uma reconhecida importância para o restabelecimento das funções dos ecossistemas e dos estoques de nutrientes perdidos da floresta madura, mas ignorados por muitos anos de taxas oficiais de desmatamento na Amazônia brasileira. Este estudo apresenta uma abordagem utilizando Análise de Imagens Baseada em Objetos Geográficos (GEOBIA) para classificar os estágios de sucessão secundária numa área com cerca de 11.124 km² na região de Santarém (Pará, Brasil). Dentre os resultados, foram produzidas 19 diferentes classificações cobrindo o período 1984 a 2016, que permitiu identificar a redução da floresta madura e da floresta secundária devido à expansão da fronteira agrícola. Outro resultado relevante foi a modelagem de uma árvore de decisão aplicável às imagens de refletância de superfície coletadas pelos satélites LANDSAT, processando esses atributos de classificação em um aplicativo de mineração de dados
The earth surface was modified in the last 50 years more than in any other period of the History, more intense and fast in the tropics by the expansion of human occupation frontiers on the mature forest. The Brazilian Amazon, characterized by alternating extractive economic cycles, exemplifies this process. Between the degraded areas abandonment and the new occupation fronts, forest regeneration takes place. The secondary forest has a recognized importance for the restoration of ecosystem functions and the nutrient stocks lost from the mature forest but ignored for many years of official deforestation rates in the Brazilian Amazon. In this study, an approach using Geographic Object-Based Imaging Analysis (GEOBIA) is presented to classify the stages of secondary succession in an area with near 11,124 km² on Santarém region (Pará State, Brazil). Among the results, 19 different classifications were produced covering the period 1984 to 2016, which allowed identify the reduction of mature forest and secondary forest due to agricultural frontier expansion. Another relevant result was the modeling of a decision tree applicable to surface reflectance images collected by the LANDSAT satellites, processing these classifications attributes in a data mining software

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Richards, Mark Andrew. "An intuitive motion-based input model for mobile devices". Queensland University of Technology, 2006. http://eprints.qut.edu.au/16556/.

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Traditional methods of input on mobile devices are cumbersome and difficult to use. Devices have become smaller, while their operating systems have become more complex, to the extent that they are approaching the level of functionality found on desktop computer operating systems. The buttons and toggle-sticks currently employed by mobile devices are a relatively poor replacement for the keyboard and mouse style user interfaces used on their desktop computer counterparts. For example, when looking at a screen image on a device, we should be able to move the device to the left to indicate we wish the image to be panned in the same direction. This research investigates a new input model based on the natural hand motions and reactions of users. The model developed by this work uses the generic embedded video cameras available on almost all current-generation mobile devices to determine how the device is being moved and maps this movement to an appropriate action. Surveys using mobile devices were undertaken to determine both the appropriateness and efficacy of such a model as well as to collect the foundational data with which to build the model. Direct mappings between motions and inputs were achieved by analysing users' motions and reactions in response to different tasks. Upon the framework being completed, a proof of concept was created upon the Windows Mobile Platform. This proof of concept leverages both DirectShow and Direct3D to track objects in the video stream, maps these objects to a three-dimensional plane, and determines device movements from this data. This input model holds the promise of being a simpler and more intuitive method for users to interact with their mobile devices, and has the added advantage that no hardware additions or modifications are required the existing mobile devices.

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Araujo, Agnes Silva de. "Cobertura da terra intraurbana para inferências sobre a qualidade de vida na cidade de Marília/SP". Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/8/8135/tde-11042016-125248/.

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O entendimento do espaço intraurbano das cidades requer a observação, identificação e compreensão da relação de padrões e formas espaciais para o desvendamento de seus conteúdos e compreensão dos processos que atuam na produção e reprodução do mesmo. Mapas temáticos de cobertura e uso da terra e de índices e indicadores sociais comumente são utilizados para adquirir informação sobre os padrões existentes na cidade, uma vez que são fontes de dados para a elaboração de diagnósticos, ordenamento e gestão dos territórios. Esta pesquisa tem como objetivo correlacionar à classificação de cobertura da terra intraurbana da cidade de Marília/SP elaborada a partir de imagens orbitais de alta resolução utilizando-se do método de análise de imagens baseada em objetos (GEOBIA) com os índices e indicadores sociais de qualidade de vida, qualidade ambiental, educacional e de nível socioeconômico para inferências sobre a qualidade de vida e a segregação socioespacial na cidade de Marília/SP. Para a espacialização e processamento dos dados quantitativos e qualitativos foram utilizadas técnicas de geoprocessamento, por meio do uso de um Sistema de Informações Geográficas, técnicas estatísticas e de sensoriamento remoto, que permitiram análises espaciais dos dados elaborados. Os resultados foram apresentados e a metodologia proposta demonstrou-se promissora para ser aplicada na atualização de informações do espaço intraurbano para subsidiar o planejamento urbano e a gestão territorial e consequentemente, contribuir para a melhoria da qualidade de vida da população.
The understanding of intraurban space in cities requires the observation and identification of the relationship between spatial patterns for the unveiling of its contents to understand the processes involved in the production and reproduction of these spaces. Thematic land cover/land use maps and social indicators maps are commonly used to acquire information on the existing spatial patterns, they are an important data source for land planning and management, and hence, are crucial in zoning projects. This research aims to correlate intraurban land cover classification maps from the city of Marília/SP developed from high resolution satellite images using the image analysis based on objects (GEOBIA) method with the indices and social indicators of quality of life, environmental quality, education and socioeconomic level for inferences about the quality of life and socio spatial segregation in the city of Marília/SP. For the spatial distribution and processing of the quantitative and qualitative data, geoprocessing techniques were applied, through the use of a Geographic Information System, statistical techniques and remote sensing, which allowed spatial analysis of data created. The results were presented and the proposed method was demonstrated promising to be applied in updating intraurban space information to support urban planning and land management and, consequently, contribute to improving the population\'s quality of life.

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Rougier, Simon. "Apport des images satellites à très haute résolution spatiale couplées à des données géographiques multi-sources pour l’analyse des espaces urbains". Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAH019/document.

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Les villes sont confrontées à de nombreuses problématiques environnementales. Leurs gestionnaires ont besoin d'outils et d'une bonne connaissance de leur territoire. Un objectif est de mieux comprendre comment s'articulent les trames grise et verte pour les analyser et les représenter. Il s'agit aussi de proposer une méthodologie pour cartographier la structure urbaine à l'échelle des tissus en tenant compte de ces trames. Les bases de données existantes ne cartographient pas la végétation de manière exhaustive. Ainsi la première étape est d'extraire la végétation arborée et herbacée à partir d'images satellites Pléiades par une analyse orientée-objet et une classification par apprentissage actif. Sur la base de ces classifications et de données multi-sources, la cartographie des tissus se base sur une démarche d'extraction de connaissances à partir d'indicateurs issus de l'urbanisme et de l'écologie du paysage. Cette méthodologie est construite sur Strasbourg puis appliquée à Rennes
Climate change presents cities with significant environmental challenges. Urban planners need decision-making tools and a better knowledge of their territory. One objective is to better understand the link between the grey and the green infrastructures in order to analyse and represent them. The second objective is to propose a methodology to map the urban structure at urban fabric scale taking into account the grey and green infrastructures. In current databases, vegetation is not mapped in an exhaustive way. Therefore the first step is to extract tree and grass vegetation using Pléiades satellite images using an object-based image analysis and an active learning classification. Based on those classifications and multi-sources data, an approach based on knowledge discovery in databases is proposed. It is focused on set of indicators mostly coming from urbanism and landscape ecology. The methodology is built on Strasbourg and applied on Rennes to validate and check its reproducibility

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40

Bellón, de la Cruz Beatriz. "Une approche multiscalaire par télédétection pour la cartographie et la caractérisation des systèmes agricoles à l’échelle régionale". Thesis, Paris, AgroParisTech, 2018. http://www.theses.fr/2018AGPT0002.

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Dans un contexte de planification de l’utilisation des terres à l’échelle régionale, la cartographie des systèmes agricoles - espèces cultivées et pratiques culturales - permet de suivre ce qui est produit, où et comment, et constitue donc un élément essentiel d’évaluation régionale de la production et de son impact sur l’environnement. La production d’information sur les systèmes agricoles nécessite généralement beaucoup de données et d’expertise. Cette information est donc très hétérogène en quantité et en qualité dans l'espace et le temps, la disponibilité et les mises à jour étant extrêmement variables selon les pays et les régions. La télédétection, de par sa capacité à fournir une information spatiale synoptique sur l’état et la dynamique de la végétation à partir des images satellitaires, constitue un outil précieux pour le suivi de l’agriculture. Toutefois, la conversion des images en produits cartographiques à l’échelle régionale reste encore du domaine de la recherche pour de nombreuses applications. Cette thèse propose des développements méthodologiques originaux dans une approche multiscalaire semi-automatique basée sur le traitement et l’analyse d’imagerie satellitaire optique pour la cartographie et la caractérisation des systèmes agricoles à l’échelle régionale. L’approche est composée de deux méthodes principales : (i) stratification régionale en unités de paysage et classification de ces unités pour produire une carte de systèmes d’utilisation agricole des terres ; (ii) segmentation à l’échelle de la parcelle et classification non supervisée des segments par une méthode de « landscape-clustering » pour produire une carte de systèmes de culture. Les méthodes ont été développées sur une région d’agriculture intensive, l’État brésilien du Tocantins, où le domaine cultivé, ainsi que les principaux systèmes d’utilisation agricole des terres et systèmes de culture ont été cartographiés avec succès à partir d’une série annuelle d’images NDVI-MODIS et d’une mosaïque d’images Landsat. La reproductibilité de l’approche a ensuite été évaluée au Burkina Faso, où les paysages sont façonnés par la petite agriculture familiale. Seul le domaine cultivé a pu être cartographié avec des résultats satisfaisants, mettant en évidence les limites de ces méthodes et de l’offre actuelle en imagerie satellitaire face aux spécificités contraignantes de ce type d’agriculture pour la télédétection. Les cartes résultantes ont été évaluées avec des données de vérité terrain et des statistiques agricoles, et comparées à d’autres produits cartographiques existants. Les résultats de cette thèse montrent le potentiel de la nouvelle méthode de stratification régionale en unités de paysage qui, à partir de séries temporelles de NDVI et combinée à la méthode de classification de « landscape-clustering », contribue à améliorer de façon significative la discrimination des espèces cultivées et des pratiques agricoles, et permet de représenter les systèmes agricoles à différents niveaux d’organisation spatiale. L’originalité des méthodes développées réside principalement dans la simplicité de leur mise en œuvre. Elles sont presque exclusivement basées sur des données satellitaires et nécessitent peu d’intervention « experte » et de données externes, ce qui leur confère un fort potentiel de reproductibilité. Cette thèse contribue ainsi, avec ces nouvelles méthodes, au développement d'outils génériques pour soutenir le suivi de l’agriculture à grande échelle et fournir des produits d’aide à la décision pour une planification raisonnée de l'utilisation des terres
: In a context of regional land-use planning, agricultural systems’ mapping - crop types and cropping practices – allows monitoring of what is being produced, where and how, and therefore represents a key element for regional assessment of the agricultural production and its environmental impact. The production of information on agricultural systems generally requires a lot of data and expertise. This information is thus very heterogeneous in quantity and quality in space and time, the availability and updates being extremely variable between countries and regions. Remote sensing, through its ability to retrieve synoptic spatial information on the state and dynamics of vegetation from satellite images, represents a valuable tool for agricultural monitoring. However, the conversion of images into regional-scale map products remains a field of research for many applications. This thesis presents original methodological developments in a semi-automatic multiscale approach based on the processing and analysis of optical satellite imagery for the mapping and characterization of agricultural systems at regional scale. The approach is composed of two main methods: (i) regional stratification into landscape units and classification of these units to produce a map of agricultural land-use systems; (ii) field-level segmentation and unsupervised classification of the segments by a “landscape-clustering” method to produce a cropping systems’ map. The methods were developed on a region of intensive agriculture, the Brazilian state of Tocantins, where the cultivated area, as well as the main agricultural land-use systems and cropping systems were successfully mapped from an annual NDVI-MODIS time series and a mosaic of Landsat images. The reproducibility of the approach was then assessed in Burkina Faso, where landscapes are shaped by the smallholder agriculture. Only the cultivated area could be mapped with satisfactory results, highlighting the limitations of these methods and the current offer in satellite imagery given the challenging specificities of this type of agriculture for remote sensing. The resulting maps were assessed with ground-truth data and agricultural statistics, and compared to other existing maps. The results of this thesis show the potential of the new method of regional stratification into landscape units which, based on NDVI time series and combined to the unsupervised “landscape-clustering” classification method, contributes to significantly improve discrimination of crop types and agricultural practices, and allows representing the agricultural systems at different levels of spatial organization. The originality of the developed methods lies mainly in the simplicity of their implementation. They are almost exclusively based on satellite data and require little “expert” intervention and external data, which gives them a high reproducibility potential. Thereupon, this thesis contributes, with these novel methods, to the development of generic tools to support large-scale monitoring of agriculture and to provide decision-support products for reasoned land-use planning

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Xu, Bo. "Object-based image analysis (OBIA) of vegetation in Great Smoky Mountains National Park". 2007. http://purl.galileo.usg.edu/uga%5Fetd/xu%5Fbo%5F200712%5Fms.

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Lübker, Tillmann. "Object-based remote sensing for modelling scenarios of rural livelihoods in the highly structured farmland surrounding Kakamega Forest, western Kenya: Object-based remote sensing for modelling scenarios of rural livelihoods in the highly structured farmland surrounding Kakamega Forest, western Kenya". Doctoral thesis, 2013. https://tud.qucosa.de/id/qucosa%3A28246.

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This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences). Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods. The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type. As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research.:1. Introduction 2. Geodata and reference data 3. Object-based image analysis (OBIA) 4. Optimization of segmentation parameters 5. Feature selection and threshold determination 6. OBIA classification: rule set development and realisation 7. Classification results 8. Spatial farmland typology 9. Spatially explicit planning scenarios of rural livelihoods 10. Discussion
Die vorliegende Arbeit untersucht räumlich-expliziten das stark strukturierte und dicht besiedelte Agrarland um den Kakamega Wald (Westkenia). Dabei kombiniert der interdisziplinäre Ansatz Methoden und Technologien verschiedener Wissenschaftsbereiche: die Fernerkundung mit der objekt-basierten Bildanalyse (OBIA), GIS und die räumlich-explizite Modellierung (Geoinformatik und Geographie) mit sozio- und agro-ökonomische Aspekten (Human- und Sozialwissenschaft) sowie der Kartographie. Zudem steht die Arbeit in Bezug zum Schutz der biologischen Vielfalt (Biologie). Ausgehend von einer Referenzdatenerfassung vor Ort und einer visuellen Bildinterpretation wurden räumlich sehr hochauflösende QuickBird-Satellitenbilddaten, die 466 km² des Agrarlandes abdecken, mit Hilfe von OBIA ausgewertet. In einem integrativen Ansatz wurden dabei statistische Verfahren und Expertenwissen kombiniert, um einen ausgefeilten Regelsatz zur Klassifizierung zu erzeugen. Das Klassifizierungsergebnis unterscheidet 15 Klassen der Landnutzung bzw. -bedeckung; zusammen mit zeitlich extrapolierten und räumlich neu verteilten Bevölkerungsdaten sowie sozio- und agro-ökonomischen Faktoren ermöglichte es, eine räumlich-explizite Typologie des Agrarlandes zu erstellen und Szenarien zum ländlichen Auskommen zu modellieren. Die Agrarlandtypologie unterscheidet zehn Landtypen: 3 Zuckerrohr-dominierte Typen (48% des Gebietes), 3 Tee-dominierte Typen (30%), 2 Übergangstypen (15%), 1 Typ steilen Geländes (2%) und 1 zentralen Typ (5%). Die Szenarien betrachten mögliche zukünftige Entwicklungen der Erträge und Preise der Hauptanbauarten Zuckerrohr, Tee und Mais. Von allen Agrarlandtypen ist der „marginal Zuckerrohr-dominierte Typ“ am besten gerüstet, um zukünftigen Problemen zu begegnen. Bezeichnend für diesen Typ sind – neben einer vergleichsweise geringen Bevölkerungsdichte – ein hoher Anteil an Nahrungsmittelanbau zusammen mit einem gemäßigten Anbau von exportorientierten Agrarprodukten. Als Teil der Forschungsarbeit werden verschiedene neuartige Methoden vorgestellt, u.a. ein neuer konzeptioneller Rahmen für das Kategorisieren von Studien zur Parameteroptimierung, die „area fitness rate“ (AFR) als neue Messgröße für Flächendiskrepanzen, die klassifikations-basierte Nächster-Nachbar Klassifizierung sowie ein Ansatz zum Bestimmen der Güte von OBIA-Klassifizierungen. Schließlich gibt die Arbeit eine Reihe von Empfehlungen und bietet vielversprechende Ausgangspunkte für weiterführende wissenschaftliche Forschungen.:1. Introduction 2. Geodata and reference data 3. Object-based image analysis (OBIA) 4. Optimization of segmentation parameters 5. Feature selection and threshold determination 6. OBIA classification: rule set development and realisation 7. Classification results 8. Spatial farmland typology 9. Spatially explicit planning scenarios of rural livelihoods 10. Discussion

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Marpu, Prashanth Reddy [Verfasser]. "Geographic object based image analysis / by Prashanth Reddy Marpu". 2009. http://d-nb.info/994521715/34.

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Ya-ChunLi y 李雅君. "Tunnel cracks detection via Geographical Object-based Image Analysis". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/74yb82.

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碩士
國立成功大學
資源工程學系
107
The inspection of tunnel lining conditions is have been carried out with many technologies, among which LiDAR is the most efficiency method for crack detection. However, it collects an immense amount of point cloud data, which could be handled manually. Automatic data processing didn’t provide enough function in determining the existence of cracks. Moreover, a majority of current tunnel crack detection software use image processing or deep learning to detect abnormalities. These methods require extensive training and time-consuming detection for accuracy of only approximately 80%. Therefore, it is an important issue to ameliorate the problems faced by LiDAR in tunnel crack detection. To improve the efficiency of auto-detection via point clouds form at tunnel linings, a combined routine is established. The point cloud data are then filtered and cropped for visibly discernible cracks to translate into image format. Image recognition for crack detection was then performed by combining image processing with three detection methods: Principal Component Analysis (PCA), Object-Based Image Analysis (OBIA), and Geographic Object-Based Image Analysis (GEOBIA). Crack measurements were produced from these image recognition results and then compared with the actual measurements to determine the level of error. The types of tunnel lining examined in this study are concrete tunnel linings and brick tunnel linings, which were evaluated using the accuracy rate, error rate, false-positive rate, false-negative rate, and Kappa coefficient after recognition via the three detection methods. From the three applied methods, GEOBIA produced the best results. This is because of its automated selection of the scale of image segmentation, along with its image recognition capability that outperforms pixel-based classification due to its implementation of elements in addition to pixels. The identification results of the two types of lining have consistent accuracy rates of over 95% and Kappa coefficients of over 0.85. In terms of measurements, the maximum error of length in the recognition results was between 4–34% and the maximum error of width was between 23–88%; the minimum width detected was 1.1 mm.

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Chou, Yu-Jung y 周佑融. "Object-Based Video Coding and Compressed Image Scene Analysis". Thesis, 1996. http://ndltd.ncl.edu.tw/handle/91387120104638036235.

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碩士
國立臺灣大學
電機工程研究所
84
Part I: Object-Based Video Coding Nowadays, conventional approaches are reaching a saturation point so that object-based approaches arise more and more attention and interest. We have developed a novel method toward very low bit rate application which is object-based. Decomposing a image into several objects simulates the function of human vision and helps to decrease the number of the motion vectors compared to MPEG. we have also proposed a method to solve some drawback of object-based motion compensation in this part. As to residue coding, that is, prediction error coding, block-based transform method in conventional algorithm is replaced by a segmentation method to eliminate the artificial blocky effects. At last part of this part, some simulation results are shown and the performance is discussed. Part II: Compressed Image Scene Analysis In the near future, efficient indexing methods will be required to handle rapidly increasing visual information, especially when visual information systems such as video databases are established. Video analysis that partitions the video into clips or extracts interesting frames is a very important initial step for video indexing. In this part, we have developed a novel method for video analysis using the macroblack type information in the MPEG framework. This method exploits the comparison operations performed in the motion estimation procedure in the MPEG framework for detecting the scene changes resulted from motion estimation will follow some specific pattern when scene changes occurs or some effects are applied. Experiments show that our approach can perform very fast scene change, flashlight and caption detection.

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Tsai, Bo-Wen y 蔡博文. "Object Tracking and Speed Measurement Based on Stereo Image Analysis". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/75010470503847732242.

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碩士
國立中正大學
電機工程研究所
94
Motion detection and object tracking of dynamic scenes have been widely used in intelligent surveillance systems. Currently, most algorithms take a single video sequence as input for two-dimensional object tracking. In this thesis, we present a three-dimensional motion tracking based on the CAM shift algorithm and stereo image analysis. Furthermore, the motion speed of the dynamic object is obtained using the time intervals for video acquisition. For the fast moving object, motion history images are used for visual tracking and speed estimation. The experimental results have shown that single object tracking can be achieved for some specific application domains.

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47

Martins, Cristiano Louriceira. "Geographic Object Based Image Analysis aplicada a dados Sentinel 2 MSI". Master's thesis, 2021. http://hdl.handle.net/10362/118074.

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O método de classificação ao objeto – Geographic Object-Based Image Analysis (GEOBIA) serviu para classificar, com recurso a dados Sentinel 2 MSI, os tipos de uso e ocupação de solo no município de Almada. Para isso foi utilizado um método de classificação supervisionado ao objeto sem recurso a classificadores estatísticos automáticos e a qualidade do mapeamento final, foi aferida pelo cálculo das seguintes métricas de precisão temática: precisão global, percentagem de erro, precisão no produtor, precisão no utilizador, erro de comissão e erro de omissão. Foram considerados como satisfatórios os resultados que apresentassem uma precisão global >= 80%, e por tema uma precisão no utilizador e produtor igualmente >= 80%. A precisão global foi 81%, a percentagem de erro 19% e os temas que cumpriram os requisitos de precisão foram: 11 Tecido urbano (92%; 90%), 12 Indústria, comércio e transportes (81%; 96%), 141 Espaços verdes urbanos (93%; 95%) e a classe “331 Praias, dunas e areais” com a precisão de utilizador e produtor de 79% e 87%, foi incluída na lista, por ter em falta apenas 1% de precisão no utilizador, mas ter o seu processo classificativo passível de ser automatizado.
The object classification method, Geographic Object-Based Image Analysis (GEOBIA) served to classify, using Sentinel 2 MSI data, the types of land use and occupation in the municipality of Almada. For this, a supervised object classification method was used without the use of automatic statistical classifiers and the quality of the final mapping was assessed by calculating the following thematic precision metrics: global precision, percentage of error, precision in the producer, precision in the user, commission error and omission error. Results with global precision > = 80% were considered satisfactory, and by theme, precision in the user and producer equally > = 80%. The overall accuracy was 81%, the error percentage 19% and the topics that met the precision requirements were: 11 Urban fabric (92%; 90%), 12 Industry, commerce and transport (81%; 96%), 141 Urban green spaces (93%; 95%) and the class “331 Beaches, dunes and sands” with user and producer precision of 79% and 87%, was included in the list, as only 1% of accuracy is missing in the user, but have its classification process capable of being automated.

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48

Peng, Shih-Yuan y 彭詩淵. "Analysis and Control for Adjusting Image AcquisitionRate Based on Object Information in Physical and Image Domains". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/73834843845442220249.

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碩士
國立臺灣大學
電機工程學研究所
94
In recent years, applications for 3-D positioning have attracted much attention. In previous applications, there is no strategy to adjust image acquisition rate, therefore, the computational load of real-time 3-D positioning and the memory for storing images in a long time will cost much when images are just captured by a higher rate. However, when objects just keep in a static state or move very slowly, the results of 3-D positioning by higher rate are just nearly the same with the lower rate. This thesis presents three control methods for adjusting image acquisition rate. All these three rate control methods adjust the timing for image acquisition by the calculated results of object velocity. The first control method (Case I) adjusts the image acquisition rate by the information of object velocity in real world. In Case I, all cameras are adjusted to a same image acquisition rate synchronously. The second control method (Case II) adjusts the image acquisition rate by the information of object velocity in image. In Case II, each camera adjusts its image acquisition rate based on its own by the information from its captured images. The third control method (Case III) which can be regarded as a combination of (Case I) and (Case II), also adjusts the image acquisition rate by the information of object velocity in image and let all cameras adjust to same image acquisition rate synchronously. In a limited memory for image storage, according to the rate control methods that proposed in this study, the recorded duration for desired scenario is longer than that with purely high image acquisition rate. In previous 3-D positioning applications, the positioning results are obtained one image by one image. However, according to the rate control methods proposed in this study, the 3-D positioning results of the moments which are not recorded can be predicted by some mathematical methods. Therefore, the computational load for 3-D positioning process can be saved. The thesis also presents the experiments of 3-D positioning processes to compare the three control methods. The image processing steps and methods of image analysis for the 3-D positioning processes used in this thesis will be introduced in the fallowing chapters. At last, experimental results for the three control methods are analyzed and compared.

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49

"Model-based computer vision: motion analysis, motion-based segmentation, 3D object recognition". 1998. http://library.cuhk.edu.hk/record=b5889626.

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by Man-lee Liu.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 143-151).
LIST OF TABLES --- p.vi
LIST OF FIGURES --- p.xii
CHAPTER
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Model-based Motion Analysis --- p.2
Chapter 1.1.1 --- With 3D-to-3D Point Correspondences --- p.4
Chapter 1.1.2 --- With 2D-to-3D Point Correspondences --- p.5
Chapter 1.1.3 --- With 2D-to-2D Point Correspondences --- p.6
Chapter 1.2 --- Motion-based Segmentation --- p.7
Chapter 1.3 --- 3D Object Recognition --- p.8
Chapter 1.4 --- Organization of the Thesis --- p.8
Chapter 2 --- Literature Review and Summary of Contributions --- p.10
Chapter 2.1 --- Model-based Motion Analysis --- p.10
Chapter 2.1.1 --- With 3D-to-3D Point Correspondences --- p.10
Chapter 2.1.2 --- With 2D-to-3D Point Correspondences --- p.13
Chapter 2.1.2.1 --- An Iterative Approach: Lowe's Algorithm --- p.18
Chapter 2.1.2.2 --- A Linear Approach: Faugeras's Algorithm --- p.19
Chapter 2.1.3 --- With 2D-to-2D Point Correspondences --- p.22
Chapter 2.2 --- Motion-based Segmentation --- p.27
Chapter 2.3 --- 3D Object Recognition --- p.28
Chapter 2.4 --- Summary of Contributions --- p.30
Chapter 3 --- Model-based Motion Analysis with 2D-to-3D Point Correspondences --- p.34
Chapter 3.1 --- A new Iterative Algorithm for the Perspective-4-point Problem: TL-algorithm --- p.34
Chapter 3.1.1 --- Algorithm --- p.35
Chapter 3.1.2 --- Experiment --- p.37
Chapter 3.1.2.1 --- Experiment using Synthetic Data --- p.38
Chapter 3.1.2.2 --- Experiment using Real Data --- p.42
Chapter 3.2 --- An Enhancement of Faugeras's Algorithm --- p.42
Chapter 3.2.1 --- Experimental Comparison between the Original Faugeras's Algorithm and the Modified One --- p.44
Chapter 3.2.1.1 --- Experiment One: Fixed Motion --- p.44
Chapter 3.2.1.2 --- Experiment Two: Using Motion Generated Ran- domly --- p.50
Chapter 3.2.2 --- Discussion --- p.54
Chapter 3.3 --- A new Linear Algorithm for the Model-based Motion Analysis: Six-point Algorithm --- p.55
Chapter 3.3.1 --- General Information of the Six-point Algorithm --- p.55
Chapter 3.3.2 --- Original Version of the Six-point Algorithm --- p.56
Chapter 3.3.2.1 --- Linear Solution Part --- p.56
Chapter 3.3.2.2 --- Constraint Satisfaction --- p.58
Use of Representation of Rotations by Quaternion --- p.62
Use of Singular Value Decomposition --- p.62
Determination of the translational matrix --- p.63
Chapter 3.3.3 --- Second Version of the Six-point Algorithm --- p.64
Chapter 3.3.4 --- Experiment --- p.65
Chapter 3.3.4.1 --- With Synthetic Data --- p.66
Experiment One: With Fixed Motion --- p.66
Experiment Two: With Motion Generated Randomly --- p.77
Chapter 3.3.4.2 --- With Real Data --- p.93
Chapter 3.3.5 --- Summary of the Six-Point Algorithm --- p.93
Chapter 3.3.6 --- A Visual Tracking System by using Six-point Algorithm --- p.95
Chapter 3.4 --- Comparison between TL-algorithm and Six-point Algorithm developed --- p.97
Chapter 3.5 --- Summary --- p.102
Chapter 4 --- Motion-based Segmentation --- p.104
Chapter 4.1 --- A new Approach with 3D-to-3D Point Correspondences --- p.104
Chapter 4.1.1 --- Algorithm --- p.105
Chapter 4.1.2 --- Experiment --- p.109
Chapter 4.2 --- A new Approach with 2D-to-3D Point Correspondences --- p.112
Chapter 4.2.1 --- Algorithm --- p.112
Chapter 4.2.2 --- Experiment --- p.116
Chapter 4.2.2.1 --- Experiment using synthetic data --- p.116
Chapter 4.2.2.2 --- Experiment using real image sequence --- p.119
Chapter 4.3 --- Summary --- p.119
Chapter 5 --- 3D Object Recognition --- p.121
Chapter 5.1 --- Proposed Algorithm for the 3D Object Recognition --- p.122
Chapter 5.1.1 --- Hypothesis step --- p.122
Chapter 5.1.2 --- Verification step --- p.124
Chapter 5.2 --- 3D Object Recognition System --- p.125
Chapter 5.2.1 --- System in Matlab: --- p.126
Chapter 5.2.2 --- System in Visual C++ --- p.129
Chapter 5.3 --- Experiment --- p.131
Chapter 5.3.1 --- System in Matlab --- p.132
Chapter 5.3.2 --- System in Visual C++ --- p.136
Chapter 5.4 --- Summary --- p.139
Chapter 6 --- Conclusions --- p.140
REFERENCES --- p.142
APPENDIX
Chapter A --- Representation of Rotations by Quaternion --- p.152
Chapter B --- Constrained Optimization --- p.154

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50

Ya-ChingHsu y 許雅菁. "Applying Object-based Image Analysis Technique and Oblique Aerial Imagery for Urban Area Classification". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/37986426021245989235.

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碩士
國立成功大學
測量及空間資訊學系碩博士班
101
With the evolution of three dimensional GIS (Geospatial Information Systems) and digital map, nowadays, cyber-city modeling becomes a major topic in related research. We can take in every city at a glance with the 3D views on the network platform and implement some spatial assessment through the cyber-city. Comparing to the 2D maps, 3D maps provide much more information and intuitive visual response. In recent years, with the development of image matching technique and camera system, cyber-city models can be reconstructed through the multi-view aerial image. In addition to use the vertical aerial image only, the oblique views provide both the top and side information of the surface objects. For this reason, the multi-view aerial images are often utilized in building modeling researches such as façade texturing and building model reconstruction. Moreover, we can also acquire amount surface points from those images with the dense image matching technique. Comparing to the aerial LiDAR points, this kind of point cloud is much cheaper and denser. In our study, we performed an object-based image classification rule set on multi-view aerial imagery in urban area to extract the semantic information of the cyber-city. The images are finally classified into Grass, Tree, Façade, Roof, Road, Window and Others classes with a hierarchical coarse to fine rule set. For classifying the surface object more correctly, we also utilized the photogrammetric point cloud which generated by the multi-view imagery to produce the auxiliary height information. Over the last decade, the object-based image analysis (OBIA) has substituted the pixel-based classification method gradually. With the multiresolution image segmentation algorithm, objects are produced by merging the pixels with shape and color homogeneity. The object contains more features such as texture or shape indices in identifying the target class that lead the classification result closer to the human interpretation result, whereas the pixel only with spectral information. In the classification, the image will separate into several parts at first, namely the “objects”, through image segmentation algorithm. Then, we defined a coarse to fine rule set to classify the objects hierarchically according to the spectrum, geometry and class-related features indices. The auxiliary feature layers which include the original image, “height map” and “gradient map” are applied for detecting the target class. Besides, we also add the beneficial “edge map” in segmentation layers. Considered that the “height map” and “gradient map” are come from the photogrammetric point cloud, we also verified their reliability and correctness when comparing to the ALS (Airborne Laser Scanning) point cloud. Our experiment result shows the overall accuracy can achieve 81% and the kappa index is 0.75 which proved that the proposed classification method has a high percent correctness, especially in separating the roof, road and façade from the severe relief displacement successfully. Moreover, the semantic classification result is significant for cyber-city modeling and 3D GIS applications.

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