Bayesian segmentation of hyperspectral images | Zendy

Adel Mohammadpour | Zendy

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Bayesian segmentation of hyperspectral images

Author(s) -

Adel Mohammadpour

Publication year - 2004

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.1835254

Subject(s) - markov chain monte carlo , artificial intelligence , markov random field , computer science , pattern recognition (psychology) , hidden markov model , segmentation , bayesian probability , hyperspectral imaging , variable order bayesian network , image segmentation , markov chain , random field , machine learning , bayesian inference , mathematics , statistics

In this paper we consider the problem of joint segmentation of hyperspectralimages in the Bayesian framework. The proposed approach is based on a HiddenMarkov Modeling (HMM) of the images with common segmentation, or equivalentlywith common hidden classification label variables which is modeled by a PottsMarkov 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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