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• W2508094752 abstract "This paper proposes a novel linear hyperspectral unmixing method based on &#x1D459;1−&#x1D459;2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on &#x1D459;1−&#x1D459;2 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 to enforce the spatial smoothness. Finally, the proposed model is solved by the extended alternating direction method of multipliers (ADMM). Experimental results on simulated and real hyperspectral datasets validate the excellent performances of the proposed method." @default.
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• W2508094752 date "2016-09-01" @default.
• W2508094752 modified "2023-10-16" @default.
• W2508094752 title "Hyperspectral unmixing based on L1-L2 sparsity and total variation" @default.
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• W2508094752 doi "https://doi.org/10.1109/icip.2016.7533181" @default.
• W2508094752 hasPublicationYear "2016" @default.
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