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In supervised and unsupervised image classification, it is known that contextual classification methods based on Markov random fields (MRFs) improve the performance of noncontextual classifiers. In th...is paper, we consider the unsupervised unmixing problem with the introduction of a new MRF. First, spectral vectors observed at mixels are assumed to follow Gaussian mixtures. Second, vectors representing fractions of categories are supposed to follow an MRF over the observed area. Then, we derive an unsupervised unmixing method, which is also useful for unsupervised classification. When evaluated using a synthetic data set and a benchmark data set for classification, the proposed method performed well.show more
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