Synthetic Aperture Radar Image Classification via Mixture Approaches

Vladimir Krylov 1 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
Abstract : In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors.
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Communication dans un congrès
IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Nov 2011, Tel Aviv, Israel. 2011
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Vladimir Krylov, Josiane Zerubia. Synthetic Aperture Radar Image Classification via Mixture Approaches. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Nov 2011, Tel Aviv, Israel. 2011. 〈inria-00625551〉

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