Hyperspectral Unmixing with Rare Endmembers via Minimax Nonnegative Matrix Factorization

2021 ◽  
Author(s):  
Timothy Marrinan ◽  
Nicolas Gillis
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Hyperspectral Unmixing

Nonnegative matrix factorization with entropy regularization for hyperspectral unmixing

2021 ◽  
Vol 42 (16) ◽  
pp. 6362-6393
Author(s):  
Junmin Liu ◽  
Shuai Yuan ◽  
Xuehu Zhu ◽  
Yifan Huang ◽  
Qian Zhao
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Hyperspectral Unmixing

A Review of the Hyperspectral Unmixing Methods that Based on Constrained NMF and Constrained Sparse Regression

2015 ◽  
Vol 713-715 ◽  
pp. 1540-1545
Author(s):  
Cheng Yong Zheng
Keyword(s):  
Image Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
The Other ◽  
Research Topics ◽  
Sparse Regression ◽  
Hyperspectral Unmixing ◽  
Hyperspectral Image Analysis

Hyperspectral unmixing (HSU) plays an important role in hyperspectral image analysis, and most of the current HSU algorithms are base on linear mixing model (LMM). This paper gives a review of two linear HSU methods that have been drawn great attention recently: one is constrained nonnegative matrix factorization (CNMF) based method, the other is constrained sparse regression (CSR) based method. We carried on the systematic summary to these two types of methods, based on which, we point out some potential research topics.

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