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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

Abstract : 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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https://hal.inria.fr/hal-00723280
Contributor : Aurélie Voisin <>
Submitted on : Wednesday, August 8, 2012 - 5:35:21 PM
Last modification on : Friday, August 23, 2019 - 3:10:07 PM
Long-term archiving on: : Friday, November 9, 2012 - 2:36:19 AM

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Aurélie Voisin, Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2013, 10 (1), pp.96-100. ⟨10.1109/LGRS.2012.2193869⟩. ⟨hal-00723280⟩

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