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Proceedings Paper

High resolution SAR-image classification by Markov random fields and finite mixtures
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Paper Abstract

In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stochastic expectation maximization approach for SAR amplitude probability density function estimation. For modeling the joint distribution from marginals corresponding to single polarimetric channels we employ copulas. The accuracy of the developed semiautomatic supervised algorithm is validated in the application of wet soil classification on several high resolution SAR images acquired by TerraSAR-X and COSMO-SkyMed.

Paper Details

Date Published: 27 January 2010
PDF: 12 pages
Proc. SPIE 7533, Computational Imaging VIII, 753308 (27 January 2010); doi: 10.1117/12.838594
Show Author Affiliations
Gabriele Moser, Univ. of Genova (Italy)
Vladimir Krylov, Lomonosov Moscow State Univ. (Russian Federation)
CR INRIA Sophia Antipolis Méditeranée (France)
Sebastiano B. Serpico, Univ. of Genova (Italy)
Josiane Zerubia, CR INRIA Sophia Antipolis Méditeranée (France)


Published in SPIE Proceedings Vol. 7533:
Computational Imaging VIII
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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