Hyperspectral Image Unmixing Based on Sparse and Minimum Volume Constrained Nonnegative Matrix Factorization

Author(s):  
Denggang Li ◽  
Shutao Li ◽  
Huali Li
2015 ◽  
Vol 713-715 ◽  
pp. 1540-1545
Author(s):  
Cheng Yong Zheng

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.


2016 ◽  
Vol 9 (4) ◽  
pp. 627-632 ◽  
Author(s):  
Yan Zhao ◽  
Zhen Zhou ◽  
Donghui Wang ◽  
Yicheng Huang ◽  
Minghua Yu

2019 ◽  
Vol 56 (16) ◽  
pp. 161001
Author(s):  
方帅 Shuai Fang ◽  
王金明 Jinming Wang ◽  
曹风云 Fengyun Cao

2015 ◽  
Author(s):  
Alexander S. Iacchetta ◽  
James R. Fienup ◽  
David T. Leisawitz ◽  
Matthew R. Bolcar

Sign in / Sign up

Export Citation Format

Share Document