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

Aurélie Voisin (1) , Vladimir A. Krylov (1) , Gabriele Moser (2) , Sebastiano B. Serpico (2) , Josiane Zerubia (1)

1 AYIN - Models of spatio-temporal structure for high-resolution image processing
2 DIBE - Department of Biophysical and Electronic Engineering [Genoa]

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Aurélie Voisin, Vladimir A. 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, 2013, 10 (1), pp.96-100. ⟨10.1109/LGRS.2012.2193869⟩. ⟨hal-00723280⟩