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• W2895054330 abstract "Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit (OMP) algorithm. At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. Experiments conducted on two real hyperspectral datasets show the superiority and effectiveness of the proposed method." @default.
• W2895054330 created "2018-10-12" @default.
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• W2895054330 date "2018-10-02" @default.
• W2895054330 modified "2023-10-16" @default.
• W2895054330 title "Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images" @default.
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• W2895054330 doi "https://doi.org/10.1155/2018/8264961" @default.
• W2895054330 hasPublicationYear "2018" @default.
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