Relevant bibliographies by topics / Object Based Image Analysis (OBIA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Object Based Image Analysis (OBIA)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Object Based Image Analysis (OBIA)"

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Kavzoglu, T., M. Yildiz Erdemir, and H. Tonbul. "A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 241–47. http://dx.doi.org/10.5194/isprsarchives-xli-b7-241-2016.

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Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
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Kavzoglu, T., M. Yildiz Erdemir, and H. Tonbul. "A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 241–47. http://dx.doi.org/10.5194/isprs-archives-xli-b7-241-2016.

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Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
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Sofyan, Agus. "Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province." AGRIFOR 17, no. 1 (March 9, 2018): 1. http://dx.doi.org/10.31293/af.v17i1.3090.

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Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.
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Kavzoglu, T., and M. Yildiz. "Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-7 (September 19, 2014): 31–37. http://dx.doi.org/10.5194/isprsannals-ii-7-31-2014.

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Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
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Zatelli, P., S. Gobbi, C. Tattoni, N. La Porta, and M. Ciolli. "OBJECT-BASED IMAGE ANALYSIS FOR HISTORIC MAPS CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W14 (August 23, 2019): 247–54. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w14-247-2019.

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<p><strong>Abstract.</strong> Heritage maps represent fundamental information for the study of the evolution of a region, especially in terms of landscape and ecologic features. Historical maps present two kinds of hurdle before they can be used in a modern GIS: they must be geometrically corrected to correspond to the datum in use and they must be classified to exploit the information they contain. This study deals the latter problem: the Historical Cadaster Map, created between 1851 and 1861, for the Trentino region in the North of Italy is available as a collection of maps in the ETRS89/UTM 32N datum. The map is a high resolution scan (230 DPI, 24 bit) of the original map and has been used in several ecological studies, since it provides detailed information not only about land property but also about land use. In the past the cadaster map has been manually digitized and for each area a set of attributes has been recorded. Since this approach is time consuming and prone to errors, automatic and semi-automatic procedures have been tested. Traditional image classification techniques, such as maximum likelihood classification, supervised or un-supervised, pixelwise and contextual, do not provide satisfactory results for many reasons: map colors are very variable within the same area, symbols and characters are used to identify cadaster parcels and locations, lines, drawn by hand on the original map, have variable thickness and colors. The availability of FOSS tools for the Object-based Image Analysis (OBIA) has made possible the application of this technique to the cadaster map. This paper describes the use of GRASS GIS and R for the implementation of the OBIA approach for the supervised classification of the historic cadaster map. It describes the determination of the optimal segments, the choice of their attributes and relevant statistics, and their classification. The result has been evaluated with respect to a manually digitized map using Cohens Kappa and the analysis of the confusion matrix. The result of the OBIA classification has also been compared to the classification of the same map using maximum likelihood classification, un-supervised and supervised, both pixelwise and contextual. The OBIA approach has provided very satisfactory results with the ability to automatically remove the background and symbols and characters, creating a ready to be used classified map. This study highlights the effectiveness of the OBIA processing chain available in the FOSS4G ecosystem, and in particular the added value of the interoperability between GRASS GIS and R.</p>
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Blaschke, T., S. Lang, D. Tiede, M. Papadakis, and A. Györi. "OBJECT-BASED IMAGE ANALYSIS BEYOND REMOTE SENSING – THE HUMAN PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 879–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-879-2016.

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We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate <i>place</i> in terms of objects - is object-based image analysis (OBIA).
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Blaschke, T., S. Lang, D. Tiede, M. Papadakis, and A. Györi. "OBJECT-BASED IMAGE ANALYSIS BEYOND REMOTE SENSING – THE HUMAN PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 879–82. http://dx.doi.org/10.5194/isprsarchives-xli-b7-879-2016.

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We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate &lt;i&gt;place&lt;/i&gt; in terms of objects - is object-based image analysis (OBIA).
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Fernandez Galarreta, J., N. Kerle, and M. Gerke. "UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning." Natural Hazards and Earth System Sciences 15, no. 6 (June 1, 2015): 1087–101. http://dx.doi.org/10.5194/nhess-15-1087-2015.

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Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.
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Fernandez Galarreta, J., N. Kerle, and M. Gerke. "UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning." Natural Hazards and Earth System Sciences Discussions 2, no. 9 (September 2, 2014): 5603–45. http://dx.doi.org/10.5194/nhessd-2-5603-2014.

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Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.
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Apriyanto, Dwi Putra, I. Nengah Surati Jaya, and Nining Puspaningsih. "Examining the object-based and pixel-based image analyses for developing stand volume estimator model." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (September 1, 2019): 1586. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1586-1596.

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In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m. The estimator models were established based on 65 sample plots both measured in the field and images. The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e<sup>0.032C</sup> with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e<sup>16.48TNDVI</sup> with a mean deviation of 5.37%.
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Dissertations / Theses on the topic "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, and 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 &quot;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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Books on the topic "Object Based Image Analysis (OBIA)"

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Blaschke, Thomas, Stefan Lang, and Geoffrey J. Hay, eds. Object-Based Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77058-9.

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Thomas, Blaschke, Gärtner Georg, Hay, Geoffrey J. (Geoffrey Joseph), 1966-, Lang, Stefan (Stefan M.), Meng Liqiu, Peterson Michael P, and SpringerLink (Online service), eds. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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1956-, Menz Gunter, ed. Object-based image analysis and treaty verification: New approaches in remote sensing - applied to nuclear facilities in Iran. Dordrecht: Springer, 2008.

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Dhawan, Atam P. A knowledge-based object recognition system for applications in the space station: NASA/KBOR annual report : final report ... Feb. 1, 1987 to Jan. 31, 1988. [Washington, DC: National Aeronautics and Space Administration, 1988.

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Object-Based Image Analysis and Treaty Verification. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6961-1.

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Blaschke, Thomas, Stefan Lang, and Geoffrey Hay. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer, 2016.

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Menz, Gunter, and Sven Nussbaum. Object-Based Image Analysis and Treaty Verification: New Approaches in Remote Sensing - Applied to Nuclear Facilities in Iran. Springer, 2010.

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United States. National Aeronautics and Space Administration., ed. A knowledge-based object recognition system for applications in the space station: NASA/KBOR annual report : final report ... Feb. 1, 1987 to Jan. 31, 1988. [Washington, DC: National Aeronautics and Space Administration, 1988.

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Book chapters on the topic "Object Based Image Analysis (OBIA)"

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Prathiba, A. P., Kriti Rastogi, Gaurav V. Jain, and V. V. Govind Kumar. "Building Footprint Extraction from Very-High-Resolution Satellite Image Using Object-Based Image Analysis (OBIA) Technique." In Lecture Notes in Civil Engineering, 517–29. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7067-0_41.

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Nussbaum, Sven. "Object-based Image Analysis." In International Safeguards and Satellite Imagery, 107–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79132-4_8.

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Nelson, Stacy A. C., and Siamak Khorram. "Object Based Image Analysis." In Image Processing and Data Analysis with ERDAS IMAGINE®, 231–47. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b21969-11.

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Gevers, T., and A. W. M. Smeulders. "Color based object recognition." In Image Analysis and Processing, 319–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_217.

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Jiang, Fangyuan, Olof Enqvist, Fredrik Kahl, and Kalle Åström. "Improved Object Detection and Pose Using Part-Based Models." In Image Analysis, 396–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_38.

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Ehlers, Arne, Florian Baumann, and Bodo Rosenhahn. "Exploiting Object Characteristics Using Custom Features for Boosting-Based Classification." In Image Analysis, 420–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_40.

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Balázs, Péter, and Mihály Gara. "An Evolutionary Approach for Object-Based Image Reconstruction Using Learnt Priors." In Image Analysis, 520–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02230-2_53.

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Pontil, Massimiliano, and Alessandro Verri. "Direct aspect-based 3-D object recognition." In Image Analysis and Processing, 300–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_136.

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Paletta, Lucas, Gerald Fritz, and Christin Seifert. "Perception-Action Based Object Detection from Local Descriptor Combination and Reinforcement Learning." In Image Analysis, 639–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_65.

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Orrite, Carlos, and Elena Pollo. "Feature-Based Scaffolding for Object Tracking." In Pattern Recognition and Image Analysis, 411–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_45.

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Conference papers on the topic "Object Based Image Analysis (OBIA)"

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Dragozi, E., Ioannis Z. Gitas, Dimitris G. Stavrakoudis, and C. Minakou. "Burn severity estimation using GeoEye imagery, object-based image analysis (OBIA), and Composite Burn Index (CBI) measurements." In Third International Conference on Remote Sensing and Geoinformation of the Environment, edited by Diofantos G. Hadjimitsis, Kyriacos Themistocleous, Silas Michaelides, and Giorgos Papadavid. SPIE, 2015. http://dx.doi.org/10.1117/12.2193149.

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Formaggio, Antonio Roberto, Matheus Alves Vieira, and Camilo Daleles Renno. "Object Based Image Analysis (OBIA) and Data Mining (DM) in Landsat time series for mapping soybean in intensive agricultural regions." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351047.

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Vasylenko, N., and I. Tishaev. "Object based image analysis for cropland mapping." In 17th International Conference on Geoinformatics - Theoretical and Applied Aspects. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201801813.

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MEZARIS, V., I. KOMPATSIARIS, and M. G. STRINTZIS. "ONTOLOGIES FOR OBJECT-BASED IMAGE RETRIEVAL." In Proceedings of the 4th European Workshop on Image Analysis for Multimedia Interactive Services. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704337_0018.

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Wang, Lu, Erxue Chen, Zengyuan Li, Wanqiang Yao, and Shiming Li. "Object-based analysis for forest inventory." In Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jinwen Tian and Jie Ma. SPIE, 2013. http://dx.doi.org/10.1117/12.2031127.

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Xie, Zhi-ping, Geng-sheng Zheng, and Gui-ming He. "Moving object extraction based on Markov random field models." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655091.

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Zuo, Zhengrong, Huafeng Chen, and Tianxu Zhang. "Study on object matching method based on Hausdorff distance." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.655283.

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Jovanovic, Dusan, Miro Govedarica, Ivana Dordevic, and Vladimir Pajic. "Object based image analysis in forestry change detection." In 2010 IEEE 8th International Symposium on Intelligent Systems and Informatics (SISY 2010). IEEE, 2010. http://dx.doi.org/10.1109/sisy.2010.5647487.

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"A GENERIC CONCEPT FOR OBJECT-BASED IMAGE ANALYSIS." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002848105300533.

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Lizarazo, Ivan, and Paul Elsner. "Fuzzy segmentation for geographic object-based image analysis." In SPIE Europe Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2009. http://dx.doi.org/10.1117/12.830477.

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Reports on the topic "Object Based Image Analysis (OBIA)"

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Diesing, M., S. Archer, J. Bremner, T. Dolphin, A. L. Downie, and C. Scougal. Drone based very-high resolution imagery analysed with geographic object-based image analysis: the perfect match for mapping intertidal habitats? Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305846.

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Hofmann, Peter, Robert Marschallinger, Michael Unterwurzacher, and Fritz Zobl. Designation of marble provenance: State-of-the-art rock fabric characterization in thin sections by object based image analysis. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0284.

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Reshitnyk, L. Y., and E. M. Rubidge. Distinguishing between two canopy-forming kelp species (Macrocystis sp. and Nereocystis sp.) on the central coast of British Columbia using object-based image analysis with WorldView-2 pansharpened imagery. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305918.

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Stelmakh, Marta. HISTORICAL CONTEXT IN THE COLLECTION OF ARTICLES BY TIMOTHY SNYDER «UKRAINIAN HISTORY, RUSSIAN POLITICS, EUROPEAN FUTURE». Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11098.

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The article examines the problem of the image formation of Ukraine in the international arena in the historical journalism of Timothy Snyder. The subject of the research is the historical context in the journalistic collection «Ukrainian History, Russian Politics, European Future». It identifies the main considerations of the author on the past of Russian-Ukrainian relations and the need to develop historical consciousness in the fight against Russian manipulation. Methodology: the comparative, historical, system analysis and other methods are used in the process of scientific research. The results of the study were obtained by analysing the author’s journalistic works and by considering the main historical themes raised by Timothy Snyder. Main results: The historical context in Timothy Snyder’s journalism is often focused on the Holodomor and the events of World War II. After all, these events are connected with the beginning of the image formation of the Ukrainian people as supporters of Nazism by the Russian authorities and the devaluation of the Ukrainians’ contribution to the establishment of peace during the Second World War. It is determined that the non-reflective attitude to history, the inability to draw parallels between the events of the past and the future leads to an ineffective response to manipulation and propaganda, which can threaten world peace. Conclusions: the realization that Russian aggression against Ukraine has its own history is a necessary aspect in the elucidation of this issue. The Eurasian Union and cooperation with the European far-right are Russian propaganda tools that discredit the Ukrainian state in the world community. Publicist Timothy Snyder points out that Europe’s future interconnects with the past, so he emphasizes the need to study and rethink history, which today has become the object of propaganda and manipulation. Significance: The results of our study will help journalists who study the historical aspect of journalistic materials and research foreign materials on Ukrainian issues. In addition, our research is necessary for Ukraine, because Russia’s aggression continues, as well as the aggressor’s propaganda, which is based on the distortion and falsification of historical events.
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