Multilevel Reweighted Sparse Hyperspectral Unmixing Using Superpixel Segmentation and Particle Swarm Optimization

Abstract

As a representative structural property, the sparsity of ground covers’ distribution in hyperspectral images (HSIs) has been extensively applied to improve spectral unmixing in years. It is worth leveraging the close relationship between the sparsity of abundances and the spatial information of HSIs to obtain more reasonable sparse unmixing results. In this letter, a novel multi-level reweighted sparse unmixing method using superpixel segmentation and particle swarm optimization is proposed. Three sparse reweighted factors are finely designed at different local and global spatial levels. The first two local reweighted factors are constructed according to the sparseness and the low-rank property of pixels’ abundances in the generated superpixels. The third global reweighted factor is given by considering the change of the sparseness of each material abundance map in the entire HSI. Then, a new sparse constraint is imposed which can effectively facilitate the correct expression of abundances’ sparsity during unmixing. Moreover, particle swarm optimization based on double swarms with dimension division is employed to solve the unmixing problem and enhance the unmixing robustness. Experimental results of both simulated and real hyperspectral data validate that the proposed method can produce accurate unsupervised sparse unmixing results.