Presentation 2015-03-16
Adaptive land classification by two-stage clustering in satellite-borne polarimetric synthetic aperture radar images
Yuto TAKIZAWA, Shang FANG, Akira HIROSE,
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Abstract(in English) We have been investigating possible methods to extract useful information from full polarimetric observation data of synthetic aperture radar. We propose self-organizing adaptive classification based on the distribution and dispersion of Poincare sphere parameters representing the state of polarization by using a self-organizing map. We employ two-stage clustering to realize the classification. Experimental results demonstrate its capability of self-organizing and discovering classification dependent on the scattering features according to the land conditions.
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Keyword(in English) Self-organizing map / synthetic aperture radar / Stokes vector / subspace condition-product clustering
Paper # MBE2014-162,NC2014-113
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Committee NC
Conference Date 2015/3/9(1days)
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Registration To Neurocomputing (NC)
Language JPN
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Title (in English) Adaptive land classification by two-stage clustering in satellite-borne polarimetric synthetic aperture radar images
Sub Title (in English)
Keyword(1) Self-organizing map
Keyword(2) synthetic aperture radar
Keyword(3) Stokes vector
Keyword(4) subspace condition-product clustering
1st Author's Name Yuto TAKIZAWA
1st Author's Affiliation Department of Electrical Engineering and Information Systems, The University of Tokyo()
2nd Author's Name Shang FANG
2nd Author's Affiliation Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications
3rd Author's Name Akira HIROSE
3rd Author's Affiliation Department of Electrical Engineering and Information Systems, The University of Tokyo
Date 2015-03-16
Paper # MBE2014-162,NC2014-113
Volume (vol) vol.114
Number (no) 515
Page pp.pp.-
#Pages 6
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