Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing

• Main Authors: Qin Jiang, Yifei Dong, Jiangtao Peng, Mei Yan, Yi Sun
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Remote Sensing
• Subjects: hyperspectral unmixing; maximum likelihood estimation; nonnegative matrix factorization
• Online Access: https://www.mdpi.com/2072-4292/13/13/2637

Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. Because the objective function of NMF is the traditional least-squares function, NMF is sensitive to noise. In order to improve the robustness of NMF, this paper proposes a maximum likelihood estimation (MLE) based NMF model (MLENMF) for unmixing of hyperspectral images (HSIs), which substitutes the least-squares objective function in traditional NMF by a robust MLE-based loss function. Experimental results on a simulated and two widely used real hyperspectral data sets demonstrate the superiority of our MLENMF over existing NMF methods.