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

Gabriele Moser; Vladimir Krylov; Sebastiano B. Serpico; Josiane Zerubia

doi:10.1117/12.838594

27 January 2010 High resolution SAR-image classification by Markov random fields and finite mixtures

Gabriele Moser, Vladimir Krylov, Sebastiano B. Serpico, Josiane Zerubia

Proceedings Volume 7533, Computational Imaging VIII; 753308 (2010) https://doi.org/10.1117/12.838594
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

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.

Citation Download Citation

Gabriele Moser, Vladimir Krylov, Sebastiano B. Serpico, and Josiane Zerubia "High resolution SAR-image classification by Markov random fields and finite mixtures", Proc. SPIE 7533, Computational Imaging VIII, 753308 (27 January 2010); https://doi.org/10.1117/12.838594

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