The Experts below are selected from a list of 2874 Experts worldwide ranked by ideXlab platform
Ioannis K Tsanis - One of the best experts on this subject based on the ideXlab platform.
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a sift based dem extraction approach using GeoEye 1 satellite stereo pairs
2019Co-Authors: Ioannis N Daliakopoulos, Ioannis K TsanisAbstract:A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m GeoEye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements.
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A SIFT-Based DEM Extraction Approach Using GeoEye-1 Satellite Stereo Pairs
2019Co-Authors: Ioannis N Daliakopoulos, Ioannis K TsanisAbstract:A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m GeoEye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements
Manuel A Aguilar - One of the best experts on this subject based on the ideXlab platform.
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classification of urban areas from GeoEye 1 imagery through texture features based on histograms of equivalent patterns
2016Co-Authors: Manuel A Aguilar, Fernando J Aguilar, Antonio Fernandez, Francesco Bianconi, Andres Miguel Garcia LorcaAbstract:AbstractA family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not i...
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object based greenhouse classification from GeoEye 1 and worldview 2 stereo imagery
2014Co-Authors: Manuel A Aguilar, Fernando J Aguilar, Francesco Bianconi, Ismael FernandezAbstract:Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages.
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comparing geometric and radiometric information from GeoEye 1 and worldview 2 multispectral imagery
2014Co-Authors: Manuel A Aguilar, Fernando J Aguilar, Maria Del Mar Saldana, Andres Miguel Garcia LorcaAbstract:A comparison between GeoEye-1 (Geo format) and WorldView-2 (Basic and Ortho Ready Standard format) multispectral (MS) imagery regarding both geometric and radiometric characteristics was carried out over the same study area. Firstly, the attainable geopositioning accuracies on seven single MS images from both very high resolution sensors were compared in the same conditions, both along the sensor orientation and orthorectification phase. The orthoimages planimetric accuracy was ranging from 1.86 m to 2.22 m. The best results were achieved in the case of GeoEye-1 MS orthoimages, nearly followed by those coming from WorldView-2 ones. Secondly, and regarding the radiometric characteristics of the both MS sensors tested, a higher digital numbers’ histogram compression in the case of WorldView-2 was found in the four common bands shared by both MS sensors, especially in the blue band. A no-reference assessment of image quality by using blur ratio was also computed in order to attain image sharpness metric. The blur ratio presented slightly lower values for the GeoEye-1 images.
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assessing geometric accuracy of the orthorectification process from GeoEye 1 and worldview 2 panchromatic images
2013Co-Authors: Manuel A Aguilar, Maria Del Mar Saldana, Fernando J AguilarAbstract:Abstract GeoEye-1 and WorldView-2 are the commercial very high resolution (VHR) satellites more innovative, unexplored and presenting the highest available resolutions nowadays. The attainable geopositioning accuracies from GeoEye-1 and WorldView-2 single panchromatic images, both along the sensor orientation and orthorectification phases, are analyzed at the same study area and by using exactly the same ancillary data. The accuracy assessment was carried out depending on the following factors: (i) type of input satellite image (GeoEye-1 Geo, WorldView-2 Ortho Ready Standard and WorldView-2 Basic), (ii) sensor orientation model used (rigorous and based on rational function), (iii) number of well-distributed ground control points (GCPs) used in the triangulation process, (iv) off-nadir viewing angle, and finally (v) vertical accuracy of the DEM employed to conduct the orthorectification process. Regardless of satellite or product, the best horizontal geopositioning accuracies were always attained by using third order 3D rational functions with vendor's rational polynomial coefficients data refined by a zero order polynomial adjustment (RPC0). Focusing on WorldView-2 products, worse accuracies were yielded from Basic images than from Ortho Ready Standard level ones. As a general rule, and for attaining sub-pixel planimetric accuracies for the orthorectified GeoEye-1 Geo and WorldView-2 Ortho Ready Standard images and using RPC0 model with 7 GCPs, users should avoid off-nadir angles higher than 20° and use a very accurate DEM.
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GeoEye 1 and worldview 2 pan sharpened imagery for object based classification in urban environments
2013Co-Authors: Manuel A Aguilar, Maria Del Mar Saldana, Fernando J AguilarAbstract:The latest breed of very high resolution VHR commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 WV2 VHR satellites over urban environments. The influence on the supervised classification accuracy was evaluated by means of an object-based statistical analysis regarding three main factors: i sensor used; ii sets of image object IO features used for classification considering spectral, geometry, texture, and elevation features; and iii size of training samples to feed the classifier nearest neighbour NN. The new spectral bands of WV2 Coastal, Yellow, Red Edge, and Near Infrared-2 did not improve the benchmark established from GeoEye-1. The best overall accuracy for GeoEye-1 close to 89% was attained by using together spectral and elevation features, whereas the highest overall accuracy for WV2 83% was achieved by adding textural features to the previous ones. In the case of buildings classification, the normalized digital surface model computed from light detection and ranging data was the most valuable feature, achieving producer's and user's accuracies close to 95% and 91% for GeoEye-1 and VW2, respectively. Last but not least and regarding the size of the training samples, the rule of ‘the larger the better' was true but, based on statistical analysis, the ideal choice would be variable depending on both each satellite and target class. In short, 20 training IOs per class would be enough if the NN classifier was applied on pan-sharpened orthoimages from both GeoEye-1 and WV2.
Liping Zhao - One of the best experts on this subject based on the ideXlab platform.
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geolocation accuracy evaluation of GeoEye 1 stereo image pair
2011Co-Authors: Wei Wang, Liping ZhaoAbstract:In this paper, 5 schemes of compensation models for Rational Function Model (RFM) is proposed to improve stereo geolocation accuracy of GeoEye-1. And experimentation is carried out using stereo pair of Hobart, Australia. The results show that the accuracy of 5 schemes is similar, and with high accurate and well-distributed Ground Control Points (GCPs) the planimetry and elevation accuracy of GeoEye-1 stereo imagery pairs are both better than 0.5-meter and meet the specification for 1:5,000 topographic map of China.
Nicholas M. Holden - One of the best experts on this subject based on the ideXlab platform.
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detecting peatland drains with object based image analysis and GeoEye 1 imagery
2017Co-Authors: J Connolly, Nicholas M. HoldenAbstract:Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (GeoEye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality (CCQ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95–97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from “wetlands drainage and rewetting”. The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .
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Detecting peatland drains with Object Based Image Analysis and GeoEye-1 imagery
2017Co-Authors: J Connolly, Nicholas M. HoldenAbstract:BACKGROUND Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (GeoEye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality (CCQ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. RESULTS The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. CONCLUSIONS The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .
Fernando J Aguilar - One of the best experts on this subject based on the ideXlab platform.
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classification of urban areas from GeoEye 1 imagery through texture features based on histograms of equivalent patterns
2016Co-Authors: Manuel A Aguilar, Fernando J Aguilar, Antonio Fernandez, Francesco Bianconi, Andres Miguel Garcia LorcaAbstract:AbstractA family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not i...
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object based greenhouse classification from GeoEye 1 and worldview 2 stereo imagery
2014Co-Authors: Manuel A Aguilar, Fernando J Aguilar, Francesco Bianconi, Ismael FernandezAbstract:Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages.
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comparing geometric and radiometric information from GeoEye 1 and worldview 2 multispectral imagery
2014Co-Authors: Manuel A Aguilar, Fernando J Aguilar, Maria Del Mar Saldana, Andres Miguel Garcia LorcaAbstract:A comparison between GeoEye-1 (Geo format) and WorldView-2 (Basic and Ortho Ready Standard format) multispectral (MS) imagery regarding both geometric and radiometric characteristics was carried out over the same study area. Firstly, the attainable geopositioning accuracies on seven single MS images from both very high resolution sensors were compared in the same conditions, both along the sensor orientation and orthorectification phase. The orthoimages planimetric accuracy was ranging from 1.86 m to 2.22 m. The best results were achieved in the case of GeoEye-1 MS orthoimages, nearly followed by those coming from WorldView-2 ones. Secondly, and regarding the radiometric characteristics of the both MS sensors tested, a higher digital numbers’ histogram compression in the case of WorldView-2 was found in the four common bands shared by both MS sensors, especially in the blue band. A no-reference assessment of image quality by using blur ratio was also computed in order to attain image sharpness metric. The blur ratio presented slightly lower values for the GeoEye-1 images.
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assessing geometric accuracy of the orthorectification process from GeoEye 1 and worldview 2 panchromatic images
2013Co-Authors: Manuel A Aguilar, Maria Del Mar Saldana, Fernando J AguilarAbstract:Abstract GeoEye-1 and WorldView-2 are the commercial very high resolution (VHR) satellites more innovative, unexplored and presenting the highest available resolutions nowadays. The attainable geopositioning accuracies from GeoEye-1 and WorldView-2 single panchromatic images, both along the sensor orientation and orthorectification phases, are analyzed at the same study area and by using exactly the same ancillary data. The accuracy assessment was carried out depending on the following factors: (i) type of input satellite image (GeoEye-1 Geo, WorldView-2 Ortho Ready Standard and WorldView-2 Basic), (ii) sensor orientation model used (rigorous and based on rational function), (iii) number of well-distributed ground control points (GCPs) used in the triangulation process, (iv) off-nadir viewing angle, and finally (v) vertical accuracy of the DEM employed to conduct the orthorectification process. Regardless of satellite or product, the best horizontal geopositioning accuracies were always attained by using third order 3D rational functions with vendor's rational polynomial coefficients data refined by a zero order polynomial adjustment (RPC0). Focusing on WorldView-2 products, worse accuracies were yielded from Basic images than from Ortho Ready Standard level ones. As a general rule, and for attaining sub-pixel planimetric accuracies for the orthorectified GeoEye-1 Geo and WorldView-2 Ortho Ready Standard images and using RPC0 model with 7 GCPs, users should avoid off-nadir angles higher than 20° and use a very accurate DEM.
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GeoEye 1 and worldview 2 pan sharpened imagery for object based classification in urban environments
2013Co-Authors: Manuel A Aguilar, Maria Del Mar Saldana, Fernando J AguilarAbstract:The latest breed of very high resolution VHR commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 WV2 VHR satellites over urban environments. The influence on the supervised classification accuracy was evaluated by means of an object-based statistical analysis regarding three main factors: i sensor used; ii sets of image object IO features used for classification considering spectral, geometry, texture, and elevation features; and iii size of training samples to feed the classifier nearest neighbour NN. The new spectral bands of WV2 Coastal, Yellow, Red Edge, and Near Infrared-2 did not improve the benchmark established from GeoEye-1. The best overall accuracy for GeoEye-1 close to 89% was attained by using together spectral and elevation features, whereas the highest overall accuracy for WV2 83% was achieved by adding textural features to the previous ones. In the case of buildings classification, the normalized digital surface model computed from light detection and ranging data was the most valuable feature, achieving producer's and user's accuracies close to 95% and 91% for GeoEye-1 and VW2, respectively. Last but not least and regarding the size of the training samples, the rule of ‘the larger the better' was true but, based on statistical analysis, the ideal choice would be variable depending on both each satellite and target class. In short, 20 training IOs per class would be enough if the NN classifier was applied on pan-sharpened orthoimages from both GeoEye-1 and WV2.