Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random FieldsReportar como inadecuado




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1 ARIANA - Inverse problems in earth monitoring CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes 2 Lomonosov Moscow State University - Faculty of Computational Mathematics and Cybernetics 3 DIBE - Department of Biophysical and Electronic Engineering Genoa

Abstract : In this paper we develop a supervised classification approach for medium and high resolution multichannel synthetic aperture radar SAR amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and a Markov random field MRF approach to Bayesian classification. The novelty of this research is in introduction of copulas to classification of D-channel SAR, with D>2, within the mainframe of finite mixtures - MRF approach. This generalization results in a flexible and well performing multichannel SAR classification technique. Its accuracy is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques.

Keywords : multichannel SAR amplitude classification dictionary probability density function estimation Markov random field copula





Autor: Vladimir Krylov - Gabriele Moser - Sebastiano B. Serpico - Josiane Zerubia -

Fuente: https://hal.archives-ouvertes.fr/



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