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Article: A quantitative analysis of virtual endmembers' increased impact on the collinearity effect in spectral unmixing

TitleA quantitative analysis of virtual endmembers' increased impact on the collinearity effect in spectral unmixing
Authors
KeywordsCollinearity problem
spectral mixture analysis (SMA)
hyperspectral data
linear spectral mixture analysis (LSMA)
nonlinear spectral mixture analysis (NSMA)
Issue Date2011
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2011, v. 49, n. 8, p. 2945-2956 How to Cite?
AbstractIn 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. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/266921
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Xuehong-
dc.contributor.authorChen, Jin-
dc.contributor.authorJia, Xiuping-
dc.contributor.authorSomers, Ben-
dc.contributor.authorWu, Jin-
dc.contributor.authorCoppin, Pol-
dc.date.accessioned2019-01-31T07:19:59Z-
dc.date.available2019-01-31T07:19:59Z-
dc.date.issued2011-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2011, v. 49, n. 8, p. 2945-2956-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/266921-
dc.description.abstractIn 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. © 2011 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectCollinearity problem-
dc.subjectspectral mixture analysis (SMA)-
dc.subjecthyperspectral data-
dc.subjectlinear spectral mixture analysis (LSMA)-
dc.subjectnonlinear spectral mixture analysis (NSMA)-
dc.titleA quantitative analysis of virtual endmembers' increased impact on the collinearity effect in spectral unmixing-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2011.2121073-
dc.identifier.scopuseid_2-s2.0-79960926086-
dc.identifier.volume49-
dc.identifier.issue8-
dc.identifier.spage2945-
dc.identifier.epage2956-
dc.identifier.isiWOS:000293709200013-
dc.identifier.issnl0196-2892-

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