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Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features

Abstract : This paper addresses the problem of the classification of very high resolution (VHR) SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the greylevel co-occurrency method, are also integrated in the technique, as they allow to improve the discrimination of urban areas. Copulas are applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed and TerraSAR-X images point out the accuracy of the proposed method, also as compared with previous contextual classifiers.
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Submitted on : Thursday, September 9, 2010 - 11:50:44 AM
Last modification on : Friday, February 4, 2022 - 3:18:06 AM
Long-term archiving on: : Friday, December 10, 2010 - 2:49:51 AM

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Aurélie Voisin, Gabriele Moser, Vladimir Krylov, Sebastiano B. Serpico, Josiane Zerubia. Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features. SPIE Remote Sensing, Sep 2010, Toulouse, France. ⟨10.1117/12.865023⟩. ⟨inria-00516333⟩

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