Bayesian segmentation of hyperspectral images

Adel Mohammadpour (1) , Olivier Féron (1, 2) , Ali Mohammad-Djafari (1)

1 L2S - Laboratoire des signaux et systèmes
2 LCTS - Laboratoire des Composites Thermostructuraux

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.

Domaines: Physique mathématique [math-ph]; Physique mathématique [math-ph]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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