Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing

• Main Authors: Risheng Huang, Xiaorun Li, Liaoying Zhao
• Format: Article
• Language: English
• Published: MDPI AG 2017-10-01
• Series: Remote Sensing
• Subjects: nonnegative matrix factorization; data-guided constraints; sparseness; evenness
• Online Access: https://www.mdpi.com/2072-4292/9/10/1074

Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L 1 / 2 and L 2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may possess different sparsity levels across locations. The problem remains as to how to impose constraints accordingly when the level of sparsity varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The DGC-NMF imposes on the unknown abundance vector of each pixel with either an L 1 / 2 constraint or an L 2 constraint according to its estimated mixture level. Experiments on the synthetic data and real hyperspectral data validate the proposed algorithm.