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Recent work has shown that existing powerful Bayesian hyperspectral unmixing algorithms can be significantly improved by incorporating the inherent local spatial correlations between pixel class labels via the use of Markov random fields. We here propose a new Bayesian approach to joint hyperspectral unmixing and image classification such that the previous assumption of stochastic abundance vectors...
A valid multiscale classification method of synthetic aperture radar (SAR) imagery is proposed based on multiscale technology and Markov random field (MRF) mode. Firstly, we employ multiscale autoregressive model for extracting the feature of SAR image. which is modeled by Markov random field (MRF) Model that relies on the Gaussian distribution. Secondly, using the joint probability distribution in...
In this paper we present a Markovian method of classification of the satellite images, this method is based on a minimization of the posterior energy by the ICM method (iterated conditional mode) with the introduction of constraints of the spatial context. The originality of our method is the variability over the iterations of a temperature factor like in the simulated annealing algorithm (SA), indeed,...
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