Advanced algorithms for Bayesian classification in high dimensional spaces with applications in hyperspectral image segmentation

This paper proposes advanced algorithms for building different components of the Bayesian classification setup, with focus in high dimensional spaces. The mean field (MF) theory is used with support vector machines (SVM) to overcome the curse of dimensionality problem associated with the estimation of class conditional probabilities in high dimensional spaces. The Markov random fields (MRF) model is used to implement the contextual interaction of classes in an image. A new algorithm which uses SVM, in a regression prospective, is proposed for estimating the parameters of the MRF model. An iterative setup for the Bayesian image segmentation setup is proposed which maximizes the overall likelihoods of the defined classes in the image. Experimental results using a real hyperspectral remote sensing image illustrate the outstanding performance of the proposed algorithms.