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Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model

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5 Author(s)
Voisin, A. ; Ayin team, INRIA, Sophia Antipolis Cedex , France ; Krylov, V.A. ; Moser, G. ; Serpico, S.B.
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This letter addresses the problem of classifying synthetic aperture radar (SAR) images of urban areas by using a supervised Bayesian classification method via a contextual hierarchical approach. We develop a bivariate copula-based statistical model that combines amplitude SAR data and textural information, which is then plugged into a hierarchical Markov random field model. The contribution of this letter is thus the development of a novel hierarchical classification approach that uses a quad-tree model based on wavelet decomposition and an innovative statistical model. The performance of the developed approach is illustrated on a high-resolution satellite SAR image of urban areas.

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Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 1 )