Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas

Abstract : In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene.
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IEEE Journal of Selected Topics in Signal Processing, IEEE, 2011, 〈10.1109/JSTSP.2010.2103925〉
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Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2011, 〈10.1109/JSTSP.2010.2103925〉. 〈inria-00562326〉

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