Hyperspectral unmixing based on L1-L2 sparsity and total variation

This paper proposes a novel linear hyperspectral unmixing method based on l
₁
-l
₂
sparsity and total variation (TV) regularization. First, the enhanced sparsity based on l
₁
-l
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.