Bayesian segmentation of hyperspectral images

2007/08/22 by Mohammadpour, Adel, Féron, Olivier, Mohammad-Djafari, Ali
#Data Analysis #FOS: Physical sciences #Statistics and Probability (physics.data-an)

paper · doi:10.48550/arxiv.0708.3013

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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