23 October 2010 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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Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 78300O (2010); doi: 10.1117/12.865023
Event: SPIE Remote Sensing, 2010, Toulouse, France
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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Aurélie Voisin, Gabriele Moser, Vladimir A. 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", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300O (23 October 2010); doi: 10.1117/12.865023; https://doi.org/10.1117/12.865023
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KEYWORDS
Synthetic aperture radar

Image classification

Expectation maximization algorithms

Image resolution

Associative arrays

Feature extraction

Statistical modeling

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