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Hyperspectral images (HSIs) exhibit a limited number of labeled high-dimensional training samples, which limits the performance of some classification methods on feature extraction or feature reduction. Efficient classification techniques are desirable solutions to this problem. In this paper, we propose a supervised method based on the PCA network (PCANet) and Gaussian weighted SVM (Gaussian-SVM)...
The increased spatial and spectral resolution of hyperspectral images (HSIs) leads to very high data rates which causes difficulties in storing and transmitting all acquired data. Efficient compression techniques are desirable solution to this problem. In this paper, we propose a compression method based on the double sparsity model which has great abilities in capturing distinctive characters of...
The different reflection or absorption characteristics of ground targets at different wavelength lead to diverse energy distribution among band images of hyperspectral images (HSIs). In this paper, we propose a sparse representation of HSIs using learned multi-scale dictionaries with the help of the distinctive physical characteristics. First, each band is cut into patches of different size using...
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