  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.
