Combined Nonlocal Spatial Information and Spatial Group Sparsity in NMF for Hyperspectral Unmixing

Abstract

Unmixing is a key but difficult issue in hyperspectral image (HSI) processing, and many unmixing methods have been proposed. However, an effective introduction of the spatial context in unmixing remains a challenge but is a necessary condition for many real scene applications. In this letter, a new nonnegative matrix factorization (NMF) method that combines nonlocal spatial information with spatial group sparsity (NLNMF) is proposed. Each superpixel generated by the simple linear iterative clustering (SLIC) segmentation method was used as a group. The search region of the nonlocal means method was adaptively set using a superpixel label from each spectrum to find the similar spectra to reestimate the reference spectrum. Additionally, the sparsity of spectra in the same superpixel was considered to be the same. Experiment results for synthetic and real HSI showed that the proposed method not only can more accurately estimate the endmember and abundance compared with other unmixing methods but also has good performance regarding antinoise.