Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A
Comprehensive Review
- URL: http://arxiv.org/abs/2205.09933v1
- Date: Fri, 20 May 2022 02:48:43 GMT
- Title: Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A
Comprehensive Review
- Authors: Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, and Antonio
Plaza
- Abstract summary: Hyperspectral unmixing estimates a set of endmembers and their corresponding abundances from a hyperspectral image.
Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem.
We show how to improve NMF by utilizing the main properties of HSIs.
- Score: 25.50091058791411
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 the article with possible future directions with the purposes of
providing guidelines and inspiration to promote the development of
hyperspectral unmixing.
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