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

doi:10.1007/s12200-016-0647-7

Front. Optoelectron.

2016, Vol. 9

Issue (4) : 627-632 https://doi.org/10.1007/s12200-016-0647-7

RESEARCH ARTICLE

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

Yan ZHAO^1,²(

),Zhen ZHOU¹,Donghui WANG³,Yicheng HUANG⁴,Minghua YU⁴

¹. School of Measurement and Communication, Harbin University of Science and Technology, Harbin 150080, China
². School of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
³. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
⁴. Qiqihar Vehicle Group, Qiqihar 161000, China

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Abstract

The objective function of classical nonnegative matrix factorization (NMF) is non-convexity, which affects the obtaining of optimal solutions. In this paper, we proposed a NMF algorithm, and this algorithm was based on the constraint of endmember spectral correlation minimization and endmember spectral difference maximization. The size of endmember spectral overall-correlation was measured by the correlation function, and correlation function was defined as the sum of the absolute values of every two correlation coefficient between the spectra. In the difference constraint of the endmember spectra, the mutation of matrix trace was slowed down by introducing the natural logarithm function. Combining the image decomposition error with the influences of endmember spectra, in the objective function the projection gradient was used to achieve NMF. The effectiveness of algorithm was verified by the simulated hyperspectral images and real hyperspectral images.

Keywords hyperspectral image unmixing nonnegative matrix factorization (NMF) correlation logarithm function

Corresponding Author(s): Yan ZHAO

Just Accepted Date: 02 November 2016 Online First Date: 21 November 2016 Issue Date: 29 November 2016

Cite this article:

Yan ZHAO,Zhen ZHOU,Donghui WANG, et al. Hyperspectral image unmixing algorithm based on endmember-constrained nonnegative matrix factorization[J]. Front. Optoelectron., 2016, 9(4): 627-632.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-016-0647-7
https://academic.hep.com.cn/foe/EN/Y2016/V9/I4/627

Fig.1 Algorithm performance comparisons in different noise intensities. (a) S A D ‾ ; (b) R M S E ‾

Fig.2 Algorithm performance comparisons for different pixel numbers. (a) S A D ‾ ; (b) R M S E ‾

Fig.3 The 172th band image of Cuprite region

Fig.4 Unmixing results in Cuprite region obtained in EC-NMF algorithm. (a) Alunite; (b) buddingtonite; (c) chalcedony; (d) jarosite; (e) kaolinite#1; (f) kaolinite#2; (g) kaolinite#3; (h) montmorillonite; (i) muscovite; (j) nontronite; (k) sphene

Tab.1 SAD comparisons for different algorithms in Cuprite region

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