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

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4 Author(s)
Vladimir A. Krylov ; Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia ; Gabriele Moser ; Sebastiano B. Serpico ; Josiane Zerubia

In this paper, a novel supervised classification approach is proposed for high-resolution dual-polarization (dual-pol) 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 dictionary-based 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 dictionary-based copula-selection method 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.

Published in:

IEEE Journal of Selected Topics in Signal Processing  (Volume:5 ,  Issue: 3 )