Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing

Abstract

Spectral unmixing is an important technique for quantitatively analyzing hyperspectral remote sensing images. Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. However, acquiring the problem-dependent prior knowledge and incorporating it into NMF as effective constraints is a challenging task. In this article, a multiple clustering guided NMF unmixing approach is proposed under a self-supervised framework, which has been used to effectively learn high-level semantic information from the data with a surrogate task in many applications. Specifically, in order to provide self-supervised information to guide the NMF-based unmixing model, multiple clustering is integrated into the optimization process of NMF. Moreover, by introducing interaction between each clustering and the unmixing procedure, more accurate proximate endmember signatures and proximate abundance distributions are expected to be acquired and used to impose self-supervised constraints on endmembers and abundances, respectively. Consequently, effective prior information about endmember signatures and abundance distributions is captured and simultaneously integrated into NMF as valuable constraints to promote unmixing performance. Experiments are conducted on both synthetic data and real hyperspectral images, and the superior performance of our method is shown by comparing it with several state-of-the-art algorithms.

Keywords

• Hyperspectral image (HSI) unmixing
• multiple clustering
• nonnegative matrix factorization (NMF)
• self-supervised learning