Hyperspectral unmixing employing l1−l2 sparsity and total variation regularization

Abstract

ABSTRACTHyperspectral unmixing is essential for image analysis and quantitative applications. To further improve the accuracy of hyperspectral unmixing, we propose a novel linear hyperspectral unmixing method based on l1−l2 sparsity and total variation (TV) regularization. First, the enhanced sparsity based on the l1−l2 norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in a sparse regression unmixing model because the l1−l2 norm promotes stronger sparsity than the l1 norm. Then, TV is minimized to enforce the spatial smoothness by considering the spatial correlation between neighbouring pixels. Finally, the extended alternating direction method of multipliers (ADMM) is utilized to solve the proposed model. Experimental results on simulated and real hyperspectral datasets show that the proposed method outperforms several state-of-the-art unmixing methods.