Paper Info
Title
A Quantitative Analysis of Virtual Endmembers' Increased Impact on the Collinearity Effect in Spectral Unmixing
Abstract
In the past decades, spectral unmixing has been studied for deriving the fractions of spectrally pure materials in a mixed pixel. However, limited attention has been given to the collinearity problem in spectral mixture analysis. In this paper, quantitative analysis and detailed simulations are provided, which show that the high correlation between the endmembers, including the virtual endmembers introduced in a nonlinear model, has a strong impact on unmixing errors through inflating the Gaussian noise. While distinctive spectra with low correlations are often selected as true endmembers, the virtual endmembers formed by their product terms can be highly correlated. It is found that a virtual-endmember-based nonlinear model generally suffers more from collinearity problems compared to linear models and may not perform as expected when the Gaussian noise is high, despite its higher modeling power. Experiments were conducted on a set of in situ measured data, and the results show that the linear mixture model performs better in 61.5% of the cases.
Year
DOI
Venue
2011
10.1109/TGRS.2011.2121073
IEEE T. Geoscience and Remote Sensing
Keywords
DocType
Volume
geophysical techniques,virtual-endmember-based nonlinear model,nonlinear spectral mixture analysis (nsma),spectral mixture analysis (sma),nonlinear spectral mixture analysis,collinearity effect,collinearity problem,linear spectral mixture analysis,data analysis,image pixel,linear mixture model,spectral analysis,linear spectral mixture analysis (lsma),spectral unmixing process,hyperspectral data,geophysical image processing,quantitative analysis,gaussian noise,correlation,pixel,mathematical model
Journal
49
Issue
ISSN
Citations 
8
0196-2892
40
PageRank 
References 
Authors
1.60
15
6
Name
Order
Citations
PageRank
Xuehong Chen1736.90
Jin Chen225931.87
Xiuping Jia31424126.54
Ben Somers426930.34
Jin Wu5553.79
Pol Coppin614720.87