Bayesian segmentation of hyperspectral images

Abstract : In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.
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Contributor : Ali Mohammad-Djafari <>
Submitted on : Thursday, January 14, 2010 - 1:18:11 PM
Last modification on : Thursday, July 26, 2018 - 12:08:23 PM

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  • HAL Id : hal-00447132, version 1
  • ARXIV : 0708.3013



Adel Mohammadpour, Olivier Féron, Ali Mohammad-Djafari. Bayesian segmentation of hyperspectral images. 2007. ⟨hal-00447132⟩



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