Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24894
Title: Spectral-spatial endmember extraction by singular value decomposition for aVIRIS data
Authors: Mei, S
He, M
Mei, S
Wang, Z
Feng, D
Keywords: Hyperspectral remote sensing
Spatial-spectral
Spectral mixture analysis
Endmember extraction
Issue Date: 2009
Publisher: IEEE
Source: 4th IEEE Conference on Industrial Electronics and Applications, 2009 : ICIEA 2009, 25-27 May 2009, Xi'an, p. 1472-1476 How to cite?
Journal: 4th IEEE Conference on Industrial Electronics and Applications, 2009 : ICIEA 2009, 25-27 May 2009, Xi'an 
Abstract: Spectral Mixture Analysis (SMA) has been widely utilized for hyperspectral remote sensing image analysis and quantification to address the mixed pixel problem, in which Endmember Extraction (EE) plays an extremely important role. Distinct from the traditional EE algorithms which are only based on spectral information, a novel EE algorithm integrating spectral characteristics and spatial distribution is proposed in this paper. Purity of pixels presenting in a spatial neighborhood (SN) is examined by the Singular Value Decomposition (SVD) based on not only spectral characteristic but also spatial distribution, which effectively addresses the spectral deviation problem. Spectral deviation inside an SN is eliminated by selecting the average of the pixels in pure SNs as endmember candidates, while spectral deviation among different areas in an image is eliminated by clustering these endmember candidates. In addition, a graph theory based spatial refinement algorithm is proposed to reduce the number of endmember candidates, which can save a lot computation in the subsequent clustering step. Experimental results on AVIRIS hyperspectral data demonstrate that the proposed Spectral-spatial EE algorithm outperforms the other three popular EE algorithms, N-finder algorithm (N-FINDR), unsupervised fully constrained least squares (UFCLS) algorithm, and the automated morphological endmember extraction (AMEE) algorithm.
URI: http://hdl.handle.net/10397/24894
ISBN: 978-1-4244-2799-4
978-1-4244-2800-7 (E-ISBN)
DOI: 10.1109/ICIEA.2009.5138385
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

6
Last Week
0
Last month
0
Citations as of Apr 30, 2016

Page view(s)

17
Last Week
0
Last month
Checked on Apr 23, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.