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Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance
In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is furthe...
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Published in: | IEEE geoscience and remote sensing letters 2018-01, Vol.15 (1), p.151-155 |
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description | In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al. based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman-Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering. |
doi_str_mv | 10.1109/LGRS.2017.2778749 |
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This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al. based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman-Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2017.2778749</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Classification ; Data ; Data acquisition ; Distance ; geodesic distance ; Level measurement ; Matrix decomposition ; Methods ; Orientation ; polarimetry ; Power measurement ; Radar ; Radar data ; Radar polarimetry ; Radar scattering ; SAR (radar) ; Scattering ; Similarity ; similarity measure ; Similarity measures ; Symmetric matrices ; Synthetic aperture radar</subject><ispartof>IEEE geoscience and remote sensing letters, 2018-01, Vol.15 (1), p.151-155</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-a1e213f04283e3713824e00d30c4464fb703a551ffe2d1d9a1884c60095479d43</citedby><cites>FETCH-LOGICAL-c293t-a1e213f04283e3713824e00d30c4464fb703a551ffe2d1d9a1884c60095479d43</cites><orcidid>0000-0002-8002-5341 ; 0000-0001-6720-6108 ; 0000-0003-4377-8915</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8207778$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,54777</link.rule.ids></links><search><creatorcontrib>Ratha, Debanshu</creatorcontrib><creatorcontrib>Bhattacharya, Avik</creatorcontrib><creatorcontrib>Frery, Alejandro C.</creatorcontrib><title>Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al. based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle corrected PolSAR image. 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The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman-Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2017.2778749</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-8002-5341</orcidid><orcidid>https://orcid.org/0000-0001-6720-6108</orcidid><orcidid>https://orcid.org/0000-0003-4377-8915</orcidid></addata></record> |
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subjects | Classification Data Data acquisition Distance geodesic distance Level measurement Matrix decomposition Methods Orientation polarimetry Power measurement Radar Radar data Radar polarimetry Radar scattering SAR (radar) Scattering Similarity similarity measure Similarity measures Symmetric matrices Synthetic aperture radar |
title | Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived From a Geodesic Distance |
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