Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization

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


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

2019 ◽  
Vol 11 (20) ◽  
pp. 2416
Author(s):  
Zehua Huang ◽  
Qi Chen ◽  
Qihao Chen ◽  
Xiuguo Liu ◽  
Hao He

Hyperspectral (HS) images can provide abundant and fine spectral information on land surface. However, their applications may be limited by their narrow bandwidth and small coverage area. In this paper, we propose an HS image simulation method based on nonnegative matrix factorization (NMF), which aims at generating HS images using existing multispectral (MS) data. Our main novelty is proposing a spectral transformation matrix and new simulation method. First, we develop a spectral transformation matrix that transforms HS endmembers into MS endmembers. Second, we utilize an iteration scheme to optimize the HS and MS endmembers. The test MS image is then factorized by the MS endmembers to obtain the abundance matrix. The result image is constructed by multiplying the abundance matrix by the HS endmembers. Experiments prove that our method provides high spectral quality by combining prior spectral endmembers. The iteration schemes reduce the simulation error and improve the accuracy of the results. In comparative trials, the spectral angle, RMSE, and correlation coefficient of our method are 5.986, 284.6, and 0.905, respectively. Thus, our method outperforms other simulation methods.


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