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Unsupervised Land Cover Classification Using H//TP Space Applied to POLSAR Image Analysis
Koji KIMURA Yoshio YAMAGUCHI Hiroyoshi YAMADA
IEICE TRANSACTIONS on Communications
Publication Date: 2004/06/01
Print ISSN: 0916-8516
Type of Manuscript: PAPER
total power, anisotropy, polarimetric entropy, alphabar, Wishart distribution, iterative ML method,
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This paper takes full advantage of polarimetric scattering parameters and total power to classify polarimetric SAR image data. The parameters employed here are total power, polarimetric entropy, and averaged alpha angle (alphabar). Since these parameters are independent each other and represent all the scattering characteristics, they seem to be one of the best combinations to classify Polarimetric Synthetic Aperture Radar (POLSAR) images. Using unsupervised classification scheme with iterative Maximum Likelihood classifier, it is possible to decompose multi-look averaged coherency matrix with complex Wishart distribution effectively. The classification results are shown using Pi-SAR image data set comparing with other representative methods.