Bayesian segmentation of hyperspectral images

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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.
8 pages, 2 figures, presented at MaxEnt 2004, Inst. Max Planck, Garching, Germany

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Data Analysis, Statistics and Probability

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http://salesiana.dossiersoluciones.com/handle/123456789/136520

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0708.3013
 http://arxiv.org/abs/0708.3013

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arXiv