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This paper proposes to combine collaborative representation (CR) and sparse representation (SR) for hyperspectral image classification. SR may select too few samples that cannot well reflect within-class variations, while CR generates nonsparse code using all the atoms that may unfortunately include between-class interference. To alleviate these problems, two methods fusing CR and SR are proposed,...
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