Bayesian segmentation of hyperspectral images
Data Analysis, Statistics and Probability
2007-08-23 v1
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.
Keywords
Cite
@article{arxiv.0708.3013,
title = {Bayesian segmentation of hyperspectral images},
author = {Adel Mohammadpour and Olivier Féron and Ali Mohammad-Djafari},
journal= {arXiv preprint arXiv:0708.3013},
year = {2007}
}
Comments
8 pages, 2 figures, presented at MaxEnt 2004, Inst. Max Planck, Garching, Germany