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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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Submitted on : Wednesday, August 8, 2012 - 6:08:34 PM
Last modification on : Wednesday, February 2, 2022 - 3:51:03 PM
Long-term archiving on: : Friday, December 16, 2016 - 6:13:30 AM


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  • HAL Id : hal-00723286, version 1



Aurélie Voisin, Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model. Eusipco - 20th European Signal Processing Conference, Aug 2012, Bucarest, Romania. ⟨hal-00723286⟩



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