Robust Sparse Hyperspectral Unmixing Based on Multi-Objective Optimization

Xia Xu, Liming Wang, Bin Pan, Zhenwei Shi

Abstract: Sparse representation based hyperspectral unmixing methods have attracted increasing investigations during the past decade. Recently, multiple signal classification (MUSIC) algorithm has been verified effective in reducing the mutual coherence of the spectral library. However, the popular pre-pruning strategy by MUSIC cannot guarantee that the end-members exactly exist in the selected spectral subset when the image noise is serious. In this paper, we propose a new sparse unmixing method for hyperspectral images via integrating the pruning operation into the optimization process. The projection of the library is represented by an objective function in the proposed method. To avoid the manually settings of regularization parameters, we develop a new multi-objective based method where reconstruction error, sparsity error and the projection function are considered as three parallel objectives that could be optimized simultaneously. Experimental results have indicated the superiority of the proposed method, especially in high-noise conditions.