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This paper proposes a novel linear hyperspectral unmixing method based on 𝑙1−𝑙2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on 𝑙1−𝑙2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in sparse regression unmixing model. By taking the correlation between hyperspectral pixels into account, total variation is minimized...
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