NDLI: Impact of collinearity on linear and nonlinear spectral mixture analysis

Content Provider	IEEE Xplore Digital Library
Author	Xuehong Chen Jin Chen Xiuping Jia Jin Wu
Copyright Year	2010
Description	Author affiliation: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China (Xuehong Chen; Jin Chen; Jin Wu) \|\| School of Engineering and Information Technology, University College, The University of New South Wales, Australian Defence Force Academy, Campbell, ACT 2600, Australia (Xiuping Jia)
Abstract	Linear and nonlinear spectral mixture analysis has been studied for deriving the fractions of spectrally pure materials in a mixed pixel in the past decades. However, not much attention has been given to the collinearity problem in spectral unmixing. 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 with others. Therefore, it is found that a nonlinear model generally suffers the collinearity problem more in comparison with a linear model and may not perform as expected when the Gaussian noise is high, despite its higher modeling power. Experiments were conducted to illustrate the effects.
Starting Page	1
Ending Page	4
File Size	194708
Page Count	4
File Format	PDF
ISBN	9781424489060
e-ISBN	9781424489077
DOI	10.1109/WHISPERS.2010.5594918
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-14
Publisher Place	Iceland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Correlation Accuracy Gaussian noise Biological system modeling Linear and nonlinear Models Remote Sensing Collinearity Spectral Unmixing Pixel Remote sensing
Content Type	Text
Resource Type	Article

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