Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Qian Du¹  Antonio Plaza²  Xiuping Jia³  Heng-Chao Li⁴  Xin-Ru Feng⁵  Rui Wang⁵ 

[1] Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA;Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Polit&x00E9;School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia;School of Information Science and Technology, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China;

关键词: Deep learning;    hyperspectral unmixing;    linear mixture model;    nonnegative matrix factorization;   

DOI  :  10.1109/JSTARS.2022.3175257

来源: DOAJ

【 摘 要 】

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions, including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude this article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing.

【 授权许可】

Unknown