A. Voisin, G. Moser, V. Krylov, S. B. Serpico, J. 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. of SPIE (SPIE Symposium on Remote Sensing 2010), volume 7830, 78300O, Toulouse (France), September 20-23, 2010.
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Abstract
This paper addresses the problem of the classification of very high resolution 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 grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is 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 images point out the accuracy of the proposed method, also as compared with previous contextual classifiers.
Bibtex
@INPROCEEDINGS{Voisin10,
author = {Voisin, A. and Moser, G. and Krylov, V. and Serpico, S. B. and Zerubia, J.},
title = {Classification of very high resolution {SAR} images of urban areas by dictionary-based mixture models, copulas and {M}arkov random fields using textural features},
year = {2010},
booktitle = {Proc. of SPIE},
volume = {7830},
address = {Toulouse, France},
pages = {78300O}
}