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Proceedings Paper

Endmember extraction by pure pixel index algorithm from hyperspectral image
Author(s): Wenyu Wang; Guoyin Cai
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Paper Abstract

We describe and validate an automated methodology based on PPI to extract endmembers from images and distinct the according endmembers. Four main steps are:1)project the raw image cube to its most spectral dimensions and non-noise components by minimum noise fraction (MNF) technology; 2) use the set of spectrally distinct pixels produced by MNF as skewers for PPI, generates a list of candidates from which final endmembers can be selected; 3) an automatic selection procedure based on K-means clustering is consequently performed to determined the centriod of endmenbers. 4) linear spectral mixing model (LSMM) is used to estimate mixing coefficient. And root mean square error (RMSE) reflects the accuracy of decomposition. We use the methodology to investigate the unique properties of hyperspectral data and how spectral information can be used to identify mineralogy with the Airborne Visible/infrared imaging Spectrometer (AVIRIS) hyperspectral data from Cuprite, Nevada.

Paper Details

Date Published: 2 February 2009
PDF: 9 pages
Proc. SPIE 7157, 2008 International Conference on Optical Instruments and Technology: Advanced Sensor Technologies and Applications, 71570E (2 February 2009); doi: 10.1117/12.811953
Show Author Affiliations
Wenyu Wang, Beijing Univ. of Civil Engineering and Architecture (China)
Guoyin Cai, Beijing Univ. of Civil Engineering and Architecture (China)


Published in SPIE Proceedings Vol. 7157:
2008 International Conference on Optical Instruments and Technology: Advanced Sensor Technologies and Applications
Anbo Wang; YanBiao Liao; AiGuo Song; Yukihiro Ishii; Xudong Fan, Editor(s)

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