Hyperspectral unmixing employing l_1-l_2 sparsity and total variation regularization

摘要

Hyperspectral 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 l(1)-l(2) sparsity and total variation (TV) regularization. First, the enhanced sparsity based on the l(1)-l(2) norm is explored to depict the intrinsic sparse characteristic of the fractional abundances in a sparse regression unmixing model because the l(1)-l(2) norm promotes stronger sparsity than the l(1) 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.