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This paper introduces a new supervised technique to segment hyperspectral images: the Bayesian segmentation based on discriminative classification and on multilevel logistic (MLL) spatial prior. The approach is Bayesian and exploits both spectral and spatial information. Given a spectral vector, the posterior class probability distribution is modeled using multinomial logistic regression (MLR) which,...
We present a new semi-supervised segmentation algorithm suited to hyperspectral images, which takes full advantage of the spectral and spatial information available in the scenes. We mainly focus on problems involving very few labeled samples and a larger set of unlabeled samples. A multinomial logistic regression (MLR) is used to model the posterior class probability distributions, whereas a multilevel...
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