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Proceedings Paper

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

Paper Details

Date Published: 22 October 2010
PDF: 11 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300O (22 October 2010); doi: 10.1117/12.865023
Show Author Affiliations
Aurélie Voisin, INRIA Sophia Antipolis Méditerranée (France)
Gabriele Moser, Univ. degli Studi di Genova (Italy)
Vladimir A. Krylov, INRIA Sophia Antipolis Méditerranée (France)
Lomonosov Moscow State Univ. (Russian Federation)
Sebastiano B. Serpico, Univ. degli Studi di Genova (Italy)
Josiane Zerubia, INRIA Sophia Antipolis Méditerranée (France)

Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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