Presentation 2001/1/18
Application of Support Vector Machines for Polarimetric SAR Image Classification
Seisuke FUKUDA, Yukihiro TANIGUCHI, Ryuji NUTAHARA, Haruto HIROSAWA,
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Abstract(in English) Support vector machines (SVMs) have much attention recently as a promising tool for pattern recognition, since they are able to handle linearly nonseparable problems without difficulty, by combining the maximal margin strategy with the kernel method. This paper addresses a SVM-based approach to classification of synthetic aperture radar (SAR) images. SVMs are successfully applied to the patterns which consist of several polarimetric features or texture measures, and perform efficient image classification.
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Keyword(in English) support vector machine / polarimetry / SAR / image classification / kernel / feature vector
Paper # A・P2000-165,SANE2000-146
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Committee SANE
Conference Date 2001/1/18(1days)
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Paper Information
Registration To Space, Aeronautical and Navigational Electronics (SANE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Application of Support Vector Machines for Polarimetric SAR Image Classification
Sub Title (in English)
Keyword(1) support vector machine
Keyword(2) polarimetry
Keyword(3) SAR
Keyword(4) image classification
Keyword(5) kernel
Keyword(6) feature vector
1st Author's Name Seisuke FUKUDA
1st Author's Affiliation The Institute of Space and Astronautical Science()
2nd Author's Name Yukihiro TANIGUCHI
2nd Author's Affiliation Musashi Institute of Technology : The Institute of Space and Astronautical Science
3rd Author's Name Ryuji NUTAHARA
3rd Author's Affiliation Musashi Institute of Technology : The Institute of Space and Astronautical Science
4th Author's Name Haruto HIROSAWA
4th Author's Affiliation The Institute of Space and Astronautical Science
Date 2001/1/18
Paper # A・P2000-165,SANE2000-146
Volume (vol) vol.100
Number (no) 584
Page pp.pp.-
#Pages 7
Date of Issue