The Experts below are selected from a list of 9159 Experts worldwide ranked by ideXlab platform
Mateus Batistella - One of the best experts on this subject based on the ideXlab platform.
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a comparative analysis of alos palsar l band and radarsat 2 c band data for land cover classification in a tropical moist region
Isprs Journal of Photogrammetry and Remote Sensing, 2012Co-Authors: Emilio F Moran, Luciano Vieira Dutra, Mateus BatistellaAbstract:This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum Likelihood Classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum Likelihood Classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum Likelihood Classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.
Suresh Kumar - One of the best experts on this subject based on the ideXlab platform.
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CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA
ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2018Co-Authors: H. Sahu, Dipanwita Haldar, Abhishek Danodia, Suresh KumarAbstract:Abstract. A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different Classifiers that are maximum Likelihood Classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based Classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum Likelihood Classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47 %, 0.47 %, 28.3 %, 28.3 % and 25.5 % respectively in all the classification algorithm but root mean square error for maximum Likelihood Classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum Likelihood Classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum Likelihood Classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.
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CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA
ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2018Co-Authors: H. Sahu, Dipanwita Haldar, Abhishek Danodia, Suresh KumarAbstract:<p><strong>Abstract.</strong> A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different Classifiers that are maximum Likelihood Classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based Classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum Likelihood Classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47<span class="thinspace"></span>%, 0.47<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>% and 25.5<span class="thinspace"></span>% respectively in all the classification algorithm but root mean square error for maximum Likelihood Classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum Likelihood Classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum Likelihood Classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.</p>
Emilio F Moran - One of the best experts on this subject based on the ideXlab platform.
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a comparative analysis of alos palsar l band and radarsat 2 c band data for land cover classification in a tropical moist region
Isprs Journal of Photogrammetry and Remote Sensing, 2012Co-Authors: Emilio F Moran, Luciano Vieira Dutra, Mateus BatistellaAbstract:This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum Likelihood Classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum Likelihood Classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum Likelihood Classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.
H. Sahu - One of the best experts on this subject based on the ideXlab platform.
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CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA
ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2018Co-Authors: H. Sahu, Dipanwita Haldar, Abhishek Danodia, Suresh KumarAbstract:Abstract. A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different Classifiers that are maximum Likelihood Classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based Classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum Likelihood Classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47 %, 0.47 %, 28.3 %, 28.3 % and 25.5 % respectively in all the classification algorithm but root mean square error for maximum Likelihood Classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum Likelihood Classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum Likelihood Classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.
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CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA
ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2018Co-Authors: H. Sahu, Dipanwita Haldar, Abhishek Danodia, Suresh KumarAbstract:<p><strong>Abstract.</strong> A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different Classifiers that are maximum Likelihood Classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based Classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum Likelihood Classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47<span class="thinspace"></span>%, 0.47<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>%, 28.3<span class="thinspace"></span>% and 25.5<span class="thinspace"></span>% respectively in all the classification algorithm but root mean square error for maximum Likelihood Classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum Likelihood Classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum Likelihood Classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.</p>
S. Jaggi - One of the best experts on this subject based on the ideXlab platform.
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PC-based hardware implementation of the maximum-Likelihood Classifier for the shuttle ice detection system
Image Processing Algorithms and Techniques II, 1991Co-Authors: S. JaggiAbstract:This paper describes a PC-based near-real time implementation of a two- channel maximum Likelihood Classifier. A prototype for the detection of ice formation on the External Tank (ET) of the Space Shuttle is being developed for NASA Science and Technology Laboratory by Lockheed Engineering and Sciences Company at Stennis Space Center, MS. Various studies have been conducted to obtain regions in the mid-infrared and the infrared part of the electromagnetic spectrum that show a difference in the reflectance characteristics of the ET surface when it is covered with ice, frost or water. These studies resulted in the selection of two channels of the spectrum for differentiating between various phases of water using imagery data. The objective is to be able to do an online classification of the ET images into distinct regions denoting ice, frost, wet or dry areas. The images are acquired with an infrared camera and digitized before being processed by a computer to yield a fast color-coded pattern, with each color representing a region. A two- monitor PC-based setup is used for image processing. Various techniques for classification, both supervised and unsupervised, are being investigated for developing a methodology. This paper discusses the implementation of a supervised classification technique. The statistical distribution of the reflectance characteristics of ice, frost, water formation on Spray-on-Foam-Insulation (SOFI), that covers the ET surface, are acquired. These statistics are later used for classification. The computer can be set in either a training mode or classifying mode. In the training mode, it learns the statistics of the various classes. In the classifying mode, it produced a color-coded image denoting the respective categories of classification. The results of the Classifier are memory-mapped for efficiency. The speed of the classification process is only limited by the speed of the digital frame grabber and the software that interfaces the frame grabber to the monitor. The process has been observed to take 4 seconds for a 512 X 480 pixel image. This set-up may have applications in other areas where detection of ice and frost on surfaces is of critical importance.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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A two-channel PC-based hardware implementation of the maximum Likelihood Classifier for the Shuttle ice detection system
IEEE Proceedings of the SOUTHEASTCON '91, 1Co-Authors: S. JaggiAbstract:A PC-based near-real-time implementation of the two-channel maximum-Likelihood Classifier for the Space Shuttle ice detection system is described. A menu-drive image processing system was developed to implement the classification process. Attention is given to the system history, requirements, and testing, as well as to system implementation, spectral signatures from imagery data, and image processing hardware and software. >
