Hyperspectral image classification using multinomial logistic regression and non-local prior on hidden fields

In this paper, we present a supervised hyperspectral image segmentation method based on multinomial logistic regression and a convex formulation of a marginal maximum a posteriori (MAP) segmentation with non-local total variation prior on the hidden fields under Bayesian framework. It not only exploits the basic assumption that samples within each class approximately lie in a lower dimensional subspace, but also sidesteps the discrete nature of the image segmentation problems by modeling spatial prior with vectorial non local means on the hidden fields. Alternating direction method of multipliers (ADMM) is finally extended to solve the proposed model. The proposed algorithm is validated by real hyperspectral data set.