inria-00495557, version 1
Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields
Vladimir Krylov
1, 2Gabriele Moser 3Sebastiano B. Serpico 3Josiane Zerubia 1
International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt) (2010)
Résumé : In this paper we develop a supervised classification approach for medium and high resolution multichannel synthetic aperture radar (SAR) amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and a Markov random field (MRF) approach to Bayesian classification. The novelty of this research is in introduction of copulas to classification of D-channel SAR, with D>2, within the mainframe of finite mixtures - MRF approach. This generalization results in a flexible and well performing multichannel SAR classification technique. Its accuracy is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques.
- 1 : ARIANA (INRIA Sophia Antipolis / Laboratoire I3S)
- INRIA – Université Nice Sophia Antipolis (UNS) – CNRS : UMR7271
- 2 : Faculty of Computational Mathematics and Cybernetics (Lomonosov Moscow State University)
- Moscow State University
- 3 : Department of Biophysical and Electronic Engineering [Genoa] (DIBE)
- University of Genoa
- Domaine : Informatique/Traitement du signal et de l'image
Sciences de l'ingénieur/Traitement du signal et de l'image - Mots-clés : multichannel SAR – amplitude – classification – dictionary – probability density function estimation – Markov random field – copula
- inria-00495557, version 1
- http://hal.inria.fr/inria-00495557
- oai:hal.inria.fr:inria-00495557
- Contributeur : Vladimir Krylov
- Soumis le : Lundi 28 Juin 2010, 11:00:15
- Dernière modification le : Lundi 28 Juin 2010, 16:56:23
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