Nonlocal Low-Rank Nonnegative Tensor Factorization for Hyperspectral Unmixing

Abstract

Hyperspectral unmixing decomposes hyperspectral images (HSI) into a collection of constituent materials or end-members and their fractions, i.e., abundances. Nonnegative tensor factorization (NTF) has been utilized thanks to its ability of preserving all the information in HSI. However, NTF based unmixing only makes use of global spatial-spectral information without considering detailed local/non-local spatial information, making it vulnerable to real-world disturbance such as noises. To this end, in this paper, we extend NTF by introducing non-local low-rank constraint to abundance maps. The additional regularization on abundances facilities tensor factorization avoid being trapped into a large number of suspicious solutions, so as to preserve the non-local spatial structure on abundance maps. Experimental results on synthetic data and real-world data show that the proposed method outperforms the state-of-the-art methods.