Using principal component analysis for endmember extraction

Andreou, C; Karathanassi, V

dc.contributor.author	Andreou, C	en
dc.contributor.author	Karathanassi, V	en
dc.date.accessioned	2014-03-01T02:53:31Z
dc.date.available	2014-03-01T02:53:31Z
dc.date.issued	2011	en
dc.identifier.issn	21586276	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36379
dc.subject	endmember extraction	en
dc.subject	hyperspectral imaging	en
dc.subject	principal components	en
dc.subject	spectral unmixing	en
dc.subject.other	Dimensionality reduction	en
dc.subject.other	Endmember extraction	en
dc.subject.other	Endmember extraction algorithms	en
dc.subject.other	Endmembers	en
dc.subject.other	Hyperspectral Data	en
dc.subject.other	Hyperspectral Imaging	en
dc.subject.other	Maximum values	en
dc.subject.other	Minimum value	en
dc.subject.other	Noise levels	en
dc.subject.other	Principal Components	en
dc.subject.other	Simulated images	en
dc.subject.other	Spectral unmixing	en
dc.subject.other	Unsupervised endmember extraction	en
dc.subject.other	Algorithms	en
dc.subject.other	Remote sensing	en
dc.subject.other	Signal processing	en
dc.subject.other	Principal component analysis	en
dc.title	Using principal component analysis for endmember extraction	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/WHISPERS.2011.6080955	en
heal.identifier.secondary	http://dx.doi.org/10.1109/WHISPERS.2011.6080955	en
heal.identifier.secondary	6080955	en
heal.publicationDate	2011	en
heal.abstract	This paper introduces a new simplex-based unsupervised endmember extraction method from hyperspectral data. The method exploits the dimensionality reduction ability of the principal component analysis, and generalizes the concept that, the first generated endmember by the Simplex Growing Algorithm, is always a pixel which has either a maximum or a minimum value in the first component, to more endmembers and components. According to the method, a subset of the minimum and maximum values of the first p-1 principal components, where p is the number of the endmembers to be defined, corresponds to the vertices of the simplex which is created by the data. In order to evaluate the proposed method, simulated images with different noise levels were created. For comparison purposes, several other known endmember extraction algorithms were applied to the data and compared with the new method. Results present that the proposed method can be promising in the field of endmember extraction. © 2011 IEEE.	en
heal.journalName	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing	en
dc.identifier.doi	10.1109/WHISPERS.2011.6080955	en