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

Gabriele Moser (1) , Vladimir Krylov (2, 3) , Sebastiano B. Serpico (1) , Josiane Zerubia (2)

1 DIBE - Department of Biophysical and Electronic Engineering [Genoa]
2 ARIANA - Inverse problems in earth monitoring
3 Lomonosov Moscow State University - Faculty of Computational Mathematics and Cybernetics

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.

Keywords: SAR image classification; dictionary; amplitude probability density; stochastic expectation maximization; Markov random field; copula

Domains: Signal and Image Processing; Signal and Image processing

Gabriele Moser, Vladimir Krylov, Sebastiano B. Serpico, Josiane Zerubia. High resolution SAR-image classification by Markov random fields and finite mixtures. IS&T/SPIE Electronic Imaging, Jan 2010, San Jose, United States. ⟨inria-00442348⟩