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Classification of very high resolution SAR images of urban areas

Aurélie Voisin 1 Vladimir Krylov 1 Gabriele Moser 2 Sebastiano B. Serpico 2 Josiane Zerubia 1 
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : In the framework of the assessment of environmental risks, we propose herein a new supervised Bayesian classification method. It combines statistical image modeling with a contextual approach via hierarchical Markov random fields. This research report aims to further focus on this kind of contextual classification approach by detailing both the quad-tree mathematical model and the statistics of the observations, obtained by wavelet transform. We therefore introduce modifications to a classical Markovian single-scale algorithm that lead to more accurate classification results.
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Submitted on : Tuesday, October 11, 2011 - 12:45:37 PM
Last modification on : Thursday, August 4, 2022 - 4:52:34 PM
Long-term archiving on: : Tuesday, November 13, 2012 - 4:25:41 PM


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  • HAL Id : inria-00631038, version 1



Aurélie Voisin, Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Classification of very high resolution SAR images of urban areas. [Research Report] RR-7758, INRIA. 2011. ⟨inria-00631038⟩



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