Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model

Abstract : In this paper, we propose a novel method for the classification of the multi-sensor remote sensing imagery, which represents a vital and fairly unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model sufficiently flexible to deal with multi-source coregistered datasets at each level of the graph. The suggested supervised method relies on a two-step technique. In the first step, a joint statistical model is developed for the input images that consists of the finite mixtures of automatically chosen parametric families for single images, and multivariate copulas to model joint class-conditional statistics at each resolution. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quad-tree structure. Multi-scale features correspond to different resolution images or are extracted by discrete wavelet transforms. To obtain the classification map, we resort to an exact estimator of the marginal posterior mode.
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Communication dans un congrès
EURASIP. Eusipco - 20th European Signal Processing Conference, Aug 2012, Bucarest, Romania. 2012
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Contributeur : Aurélie Voisin <>
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Dernière modification le : samedi 27 janvier 2018 - 01:31:39
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Aurélie Voisin, Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model. EURASIP. Eusipco - 20th European Signal Processing Conference, Aug 2012, Bucarest, Romania. 2012. 〈hal-00723286〉

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