Relevant bibliographies by topics / Very high spatial resolution Pleiades imagery

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Academic literature on the topic 'Very high spatial resolution Pleiades imagery'

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

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Journal articles on the topic "Very high spatial resolution Pleiades imagery"

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James, D., A. Collin, A. Mury, and S. Costa. "VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 675–82. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-675-2020.

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Abstract. Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km2, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).
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Nurtyawan, Rian, and Nadia Fiscarina. "ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA)." International Journal of Remote Sensing and Earth Sciences (IJReSES) 17, no. 1 (August 20, 2020): 34. http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3329.

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Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.
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Prabowo, Yudhi, and Kenlo Nishida Nasahara. "DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 1 (November 5, 2019): 87. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3089.

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This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.
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Bley-Dalouman, H., F. Broust, J. Prevost, and A. Tran. "USE OF VERY HIGH SPATIAL RESOLUTION IMAGERY FOR MAPPING WOOD ENERGY POTENTIAL FROM TROPICAL MANAGED FOREST STANDS, REUNION ISLAND." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 189–94. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-189-2021.

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Abstract. The development of a sustainable wood energy chain is an essential part of ecological and energy transition in Reunion Island (Indian Ocean), where Acacia mearnsii is the main potential wood energy resource identified to date. In order to assess future wood biomass supply chain strategies, a major first issue is to gain knowledge of the spatial distribution of this species forest stands.In this study, we assessed the potential of very high spatial resolution multispectral imagery for mapping the main forest stands in a study area located the Western Highlands region, where Acacia mearnsii expands alongside Acacia heterophylla, an endemic forest species and Cryptomeria japonica, an exotic forest stand. A reference database including 150 samples of seven classes (Acacia mearnsii (mature and non-mature), Acacia heterophylla (mature and non-mature), Cryptomeria japonica, ‘herbaceous areas’, and ‘bare soils’) was used to classify a Pleiades image acquired in May 2020. Spectral and textural indices were used in an incremental classification procedure using a random classifier.The best results (Kappa = 0.84, global accuracy = 84%) were obtained for the classification using all spectral and textural bands. The resulting map enables analyzing the spatial distribution of the different forest stands.
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Tsoeleng, Lesiba Thomas, John Odindi, and Paidamwoyo Mhangara. "A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover." Remote Sensing 12, no. 7 (March 28, 2020): 1089. http://dx.doi.org/10.3390/rs12071089.

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Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques
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Batsaikhan, B., O. Lkhamjav, G. Batsaikhan, N. Batsaikhan, and B. Norovsuren. "CARBON STOCK ESTIMATION USING REMOTE SENSING DATA AND FIELD MEASUREMENT IN <i>HALOXYLON AMMODENDRON</i> DOMINANT WINTER COLD DESERT REGION OF MONGOLIA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 9–17. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-9-2020.

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Abstract. The UN-REDD Mongolia National Programme has studied about forest carbon emissions, and enhance and sustainably manage its carbon stocks, through the implementation of REDD+ activities since 2011. However, the current assessments seem to remain uncertain, the study for estimating carbon storage based on field survey are still rare. Because the Haloxylon ammodendron, where Gobi desert ecosystems are covering large areas, it is necessary to develop a modelling approach applying remote sensing. The study area is locating in Gobi-Altai province, Trans-Altai area as the south-western part of Mongolia. A total of 32 plots were established on eighth different land cover types to represent the range of variability. The study was used high spatial resolution imagery of Pleiades-1 and both of active and passive data from Sentinel-1 and Sentinel-2. The growing height in 32 plots is ranging from 20 to 460 cm with between 0.002 and 544.9 cm2 for basal area and between 526.5 and 166106.0 cm2 for canopy area, respectively. Shrub density is very high in plot 4 (n=135) and plot 5 (n=117) with low above-ground biomass 12 kg and 10.9 kg. The backscatter (dB) values of vegetated area and non-vegetated were comparable, −27.86 and −17.36 in VH polarisation and −22.72 and −10.61 in VV polarisation, respectively. Model-M1 was best demonstrated when a combination of vegetation coverage area was used as Pleiades-1 and Sentinel-2 derived vegetation cover data. For model-M9, the results were comparable to model-M1 but with lower the coefficient of determination. In this work, NDVI and MSAVI appear as a good indicator of biomass mainly because it does not saturate in sparse shrubs and is more sensitive to canopy parameters.
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Almeida, Luís, Rafael Almar, Erwin Bergsma, Etienne Berthier, Paulo Baptista, Erwan Garel, Olusegun Dada, and Bruna Alves. "Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery." Remote Sensing 11, no. 5 (March 12, 2019): 590. http://dx.doi.org/10.3390/rs11050590.

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High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection and Ranging System (LiDAR). The derived 2-m Pleiades DEM shows an overall good agreement with concurrent methods (RTK-GPS and LiDAR; correlation coefficient of 0.9), with a vertical Root Mean Squared Error (RMS error) that ranges from 0.35 to 0.48 m, after absolute coregistration to the LiDAR dataset. The largest errors (RMS error > 0.5 m) occurred in the steep dune faces, particularly at shadowed areas. This work shows that DEMs derived from sub-meter satellite imagery capture local morphological features (e.g., berm or dune shape) on a sandy beach, over a large spatial domain.
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Agrafiotis, P., and A. Georgopoulos. "COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 1–7. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-1-2015.

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This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO) provided by NCMA S.A (Hellenic Cadastre) from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD) from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO) were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.
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Hashim, H., Z. Abd Latif, and N. A. Adnan. "URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 237–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-237-2019.

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Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.
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Hariyanto, Teguh, Akbar Kurniawan, Cherie Bhekti Pribadi, and Rizal Al Amin. "Optimization of Ground Control Point (GCP) and Independent Control Point (ICP) on Orthorectification of High Resolution Satellite Imagery." E3S Web of Conferences 94 (2019): 02008. http://dx.doi.org/10.1051/e3sconf/20199402008.

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In the rapidly evolving technology era, various survey methods have been widely used one of them by remote sensing using satellite. It is known that the satellite image recording process is covered by rides (satellites) moving over the Earth's surface at hundreds of kilometers, causing satellite imagery to have geometric distortion. To reduce the effect of geometric distortion of objects on the image, geometric correction by orthorectification is done. Pleiades is a satellite of high resolution satellite image producer made by Airbus Defense & Space company. The resulting satellite imagery has a 0.5 meter spatial resolution. As a reference for the more detailed space utilization activities of space utilization arranged in the Regional Spatial Plans, Detailed Spatial Plans was created with the 1: 5000 scale map which has been governed by the Geospatial Information Agency. In the process of orthorectifying satellite imagery for this 1: 5000 scale map, ground control or Ground Control Point (GCP) is used for geometric correction and Digital Elevation Model (DEM) data. In this research, the optimal number of GCP usage for orthorectification process in Rational Function method is 21 GCP using 2nd order polynomial
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Dissertations / Theses on the topic "Very high spatial resolution Pleiades imagery"

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Crombette, Pauline. "Contribution des technologies satellitaires Pléiades à l'étude des trames vertes urbaines : entre maintien des connectivités écologiques potentielles et densification des espaces urbains." Thesis, Toulouse 2, 2016. http://www.theses.fr/2016TOU20032/document.

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En milieu urbain, la concurrence entre les enjeux de préservation de la biodiversité et de la densification du territoire est particulièrement développée. Dans une optique d’aide à la décision, une meilleure connaissance des zones les plus conflictuelles est requise. Face au constat d’insuffisance et d’inadéquation des données et des méthodes nécessaires à la cartographie des Trames vertes urbaines, notre travail s’intègre en premier lieu dans une démarche technique. Celle-ci est centrée sur la mise en place d’une méthode de traitement d’images satellitaires Très Haute Résolution Spatiale Pléiades (THRS) pour l’extraction de la végétation arborée et herbacée à l’échelle fine d’une emprise urbaine. D’abord appliquée à des données fictives, cette méthode est ensuite déployée sur quatre territoires (Toulouse, Muret, Pierrefite-Nestalas et Strasbourg). Bien que fondée sur une approche pixel, la simplicité de la méthode, qui s’appuie sur des outils libres, et les résultats obtenus (indice Kappa supérieur à 85 %) garantissent sa reproductibilité sur de vastes territoires plus ou moins urbanisés. Cette donnée de végétation est ensuite exploitée pour modéliser les connectivités écologiques potentielles du paysage urbain et périurbain toulousain. L’approche mobilise la théorie des graphes et permet d’évaluer l’impact d’un aménagement urbain sur la biodiversité. Le cas du Boulevard Urbain Nord de Toulouse est étudié. La cartographie proposée des réservoirs de biodiversité, hiérarchisés à l’aide de métriques de connectivité, est avant tout indicative. Elle est finalement confrontée à des documents d’urbanisme (Plans Locaux d’Urbanisme) afin d’obtenir une meilleure visibilité des territoires à enjeux environnementaux et urbanistiques. En fonction des enjeux fixés par les acteurs du territoire et à travers le filtre applicatif, cette thèse propose un outil robuste d’analyse et d’aide à la décision pour la gestion et la planification du territoire
In urban areas, competition between land development and ecological conservation is intense. To assist decision making, a better knowledge of those areas of interest is required. Regarding inadequacy data and methods needed for ecological network mapping in urban areas, the aim of our study is to develop a method for semi-automatic vegetation extraction with Very High Spatial Resolution Pleiades imagery (VHSR). Initially applied to training samples, the process is then be deployed to four French study areas (Toulouse, Muret, Pierrefite-Nestalas and Strasbourg). The reproducibility of this method over large urbanized areas is ensured by its simplicity and the results of a pixel-based classification (kappa coefficient higher than 85 %). This extraction workflow uses free or open-source software. This vegetation data is then used in order to model potential ecological connectivity in Toulouse’s urban and peri-urban areas. Impacts on biodiversity due to urban planning are assessed using graph theory. The “Boulevard Urbain Nord de Toulouse” project, a road infrastructure, is studied. Graph metrics have been calculated to assess the level of connectivity at habitat patches and landscape scales. We classified the importance of the patches which is cross-tabulated with planning documents (PLU, a local town planning) in order to locate conflict urban areas: between biodiversity preservation and urbanization. Depending on the issues set out by local actors and through the application filter, this thesis proposes a robust analytical tool and decision-making aid for landscape management and land planning
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Blamire, P. A. "Inferring urban land use from very high spatial resolution remotely sensed imagery." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636110.

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With the imminent launch of a new generation of very high spatial resolution satellite sensors, 1-5m image data are soon to be available. This thesis explores the potential application of these data to infer information on urban land use. Distinctions are sought between broad categories such as industrial, commercial and residential, as well as finer residential categories indicative of housing age and type. Unlike conventional classification procedures, where land cover is inferred from spectral reflectance, it has been suggested that land use requires information on the spatial and morphological properties of the principal scene objects (e.g. buildings, roads) within the image. However, this has received little formal investigation. This hypothesis is examined quantitatively using Ordnance Survey 1:1250 digital map data (an 'optimum' segmentation of the scene, without the problems of mixed pixels, misclassification, shadowing and occlusion associated with remotely sensed imagery). Differences are observed between areas of contrasting land use, in particular the structure of the road network and the specific composition of buildings (their size and shape) within an area. The ability to extract those scene objects from remotely sensed data is subsequently assessed using airborne imagery, resampled to a number of spatial resolutions between 1 and 10m, and a variety of segmentation procedures (multi-spectral classification, edge-detection and region-growing). This indicates that, while thematic accuracy increases at finer resolutions, it is not possible to extract building and road features as discrete entitles unambiguously and consistently. Further experiments using even higher resolution data (up to 25cm) exhibit similar problems. Despite this, a final set of experiments examine the structural properties of a segmented image. Using a graph-based tool, relations such as the adjacency between regions are shown to vary between areas of contrasting land use. Image structure is also successfully used as a novel means of removing 'noise' from classified imagery.
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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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Upegui, Cardona Erika. "Télédétection et épidémiologie en zone urbaine : de l'extraction de bâtiments à partir d'images satellite à très haute résolution à l'estimation de taux d'incidence." Thesis, Besançon, 2012. http://www.theses.fr/2012BESA1015/document.

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En épidémiologie, une connaissance précise des populations à risque constitue un pré requis aucalcul d'indicateurs de l’état de santé d’une communauté (taux d'incidence). Néanmoins, les effectifsde population peuvent être indisponibles, ou peu fiables, ou insuffisamment détaillés pour un usageépidémiologique.L'objectif principal de ce travail est d'obtenir des taux d'incidence en l'absence de donnéesdémographiques, à une échelle spatiale infra-communale. Les objectifs secondaires sont d'estimerles populations humaines par l'intermédiaire de données satellitaires à très haute résolution spatiale(THRS), d'évaluer l'apport de ces données THRS par rapport aux données à haute résolution spatiale(Landsat) dans un même cadre urbain (Besançon), et de mettre au point une méthodologie simple etrobuste, pour garantir son exportabilité à d'autres zones.Nous proposons une approche en trois étapes, fondée sur la corrélation existant entre la densité depopulation et la morphologie urbaine. La première étape consiste à extraire des bâtiments à partirdes données télédétection THRS. Ces bâtiments sont utilisés dans la deuxième étape pour modéliserla population. A leur tour, ces populations servent de dénominateur, lors de la dernière étape, pourcalculer des taux d’incidence (cancers). Des données de référence sont utilisées à chaque étape pourévaluer les performances de notre méthodologie.Les résultats obtenus soulignent le potentiel de la télédétection pour mesurer l'état de santé d'unecommunauté (sous la forme de taux bruts d’incidence) à une échelle géographique fine. Ces tauxd'incidence estimés peuvent alors constituer des éléments de décision pour mieux adapter l'offre desoins aux besoin de santé, même en l'absence de données démographiques
In epidemiology, a precise knowledge of populations at risk is a prerequisite for calculating state ofhealth indicators of a community (incidence rates). The population data, however, may beunavailable, unreliable, or insufficiently detailed for epidemiological use.The main objective of this research is to estimate incidence rates, in cases of absence of demographicdata, at an infra-communal scale. The secondary objectives are to estimate the human populationthrough satellite data at very high spatial resolution (VHSR), to assess the contribution of this data(VHSR) compared with high spatial resolution data (Landsat) in a same urban framework (Besançon),and to develop a simple and robust methodology to ensure its exportability to other areas.We proposed a three-step approach based on the correlation between population density and urbanmorphology. The first step is to extract buildings from VHSR imagery data. These buildings are thenused in the second step to model the population data. Finally, this population data is used as thedenominator to calculate incidence rates (cancers). Reference data are used at each step to assessthe performance of our methodology.The results obtained highlight the potential of remote sensing to measure the state of health of acommunity (in the form of crude incidence rates) at a fine geographical scale. These estimatedincidence rates can be utilized as elements of decision to adapt better customized healthcare withrespect to the health needs of a given community, even in the absence of demographic data
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Bromová, Petra. "Analýza hustoty lesních porostů s využitím texturálních příznaků snímků vysokého prostorového rozlišení a dat leteckého laserového skenování." Master's thesis, 2012. http://www.nusl.cz/ntk/nusl-306713.

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Analysis of forest canopy density based on textural features of high resolution imagery and airborne laser scanning data Abstract The objective of this thesis is to assess the forest canopy density in the Šumava Mountains, Czech Republic. The spruce forests in this area have been suffering from the bark beetle outbreak for almost 20 years resulting in a mixture of dead and young trees, mature forest stands and peat bogs. The canopy density was evaluated using a very high spatial resolution panchromatic imagery and low point density LiDAR, combined with an object oriented approach. The classification based on three GLCM texture measures (contrast, entropy and correlation), which were derived from the image objects, resulted in a kappa index of accuracy of 0.45. Adding the information from the LiDAR data, the accuracy of the classification improved up to 0.95.
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Books on the topic "Very high spatial resolution Pleiades imagery"

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Hlavka, Christine A. Unmixing AVHRR imagery to assess clearcuts and forest regrowth in Oregon. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Hlavka, Christine A. Unmixing AVHRR imagery to assess clearcuts and forest regrowth in Oregon. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Book chapters on the topic "Very high spatial resolution Pleiades imagery"

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Pacifici, Fabio, Georgios K. Ouzounis, Lionel Gueguen, Giovanni Marchisio, and William J. Emery. "Very High Spatial Resolution Optical Imagery: Tree-Based Methods and Multi-temporal Models for Mining and Analysis." In Mathematical Models for Remote Sensing Image Processing, 81–135. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66330-2_3.

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Lasaponara, Rosa, and Nicola Masini. "Facing the Archaeological Looting in Peru by Using Very High Resolution Satellite Imagery and Local Spatial Autocorrelation Statistics." In Computational Science and Its Applications – ICCSA 2010, 254–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12156-2_19.

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Chrysoulakis, Nektarios, Poulicos Prastacos, and Constantinos Cartalis. "Development of a Decision Support Tool for Technological Risk Management with Remote Sensing and GIS." In Information Systems for Sustainable Development, 342–53. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-342-5.ch022.

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Abstract:
In this study, a GIS based decision support tool is proposed for the support of technological risk management by integrating moderate and high spatial resolution satellite imagery with in-situ vector data. The Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites has been used for the detection of fire as well as for the detection and monitoring of plumes caused by major technological accidents. The Thematic Mapper (TM) on board the Landsat satellite has been used for the depiction of the urban areas and the main road network as well as for the estimation of the spatial distribution of vegetation in the study area. A major technological accident scenario was developed for the broader area of Athens (Greece) in order to present the functionality of the GIS tool for the support of decision making during the crisis, as well as for the assessment of the accident’s impact on the natural and human environment.
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Conference papers on the topic "Very high spatial resolution Pleiades imagery"

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Liang, Haolin, and Shawn Newsam. "Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery." In SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356471.3365241.

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Chang, Chew Wai, Cheng Hua Shi, Soo Chin Liew, and Leong Keong Kwoh. "Land cover classification of very high spatial resolution satelite imagery." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723376.

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Zhang, Kongwen, Mike Curran, Justin Robinson, and Baoxin Hu. "Long Term Soil Productivity study using very high spatial resolution imagery." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6721202.

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Scarsi, Andrea, William J. Emery, Gabriele Moser, Fabio Pacifici, and Sebastiano B. Serpico. "An automated flood detection framework for very high spatial resolution imagery." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947607.

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Keramitsoglou, Iphigenia, Haralambos Sarimveis, Chris T. Kiranoudis, and Nicolaos Sifakis. "Ecosystem classification using artificial intelligence neural networks and very high spatial resolution satellite imagery." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Jose F. Moreno, and Alfonso Calera. SPIE, 2004. http://dx.doi.org/10.1117/12.511041.

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Zhang, Lan, Bo Zhong, and Aixia Yang. "Building Change Detection using Object-Oriented LBP Feature Map in Very High Spatial Resolution Imagery." In 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp). IEEE, 2019. http://dx.doi.org/10.1109/multi-temp.2019.8866919.

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Keramitsoglou, Iphigenia, Charalambos Kontoes, Panagiotis Elias, Nicolaos Sifakis, Eleni Fitoka, and Stefan Weiers. "Kernel-based reclassification algorithm applied on very high spatial resolution satellite imagery of complex ecosystems." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Jose F. Moreno, and Alfonso Calera. SPIE, 2004. http://dx.doi.org/10.1117/12.511071.

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Laneve, G., G. Santilli, and I. Lingenfelder. "Development of Automatic Techniques for Refugee Camps Monitoring using Very High Spatial Resolution (VHSR) Satellite Imagery." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.216.

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Li, Peijun, Haiqing Xu, Shuang Liu, and Jiancong Guo. "Urban building damage detection from very high resolution imagery using one-class SVM and spatial relations." In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5417719.

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Zhao, Yan, Jingfa Zhang, and Leihua Yao. "Object-oriented information extraction and evaluation of seismic damage of buildings using very high spatial resolution imagery." In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729737.

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