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Generalized bilinear model (GBM) has been widely used for nonlinear hyperspectral image unmixing. However, it does not take the sparse information of abundance into account, which is a significant characteristic resulting from the correlation of hyperspectral data. This paper aims to extend the GBM by incorporating the sparsity constraint of abundance matrix with the semi-nonnegative matrix factorization,...
Mostly, in image segmentation, we do not know the prior knowledge of the number of classes, while many clustering approaches need this prior knowledge. This fact makes the segmentation more difficult. In this paper, we introduce a complete unsupervised approach based on Gaussian mixture models, namely complete unsupervised learning of mixture models (LMM) for image segmentation. Firstly, a new feature...
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