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Proceedings Paper

Convex cone-based endmember extraction for hyperspectral imagery
Author(s): Wei Xiong; Ching Tsorng Tsai; Ching Wen Yang; Chein-I Chang
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Paper Abstract

N-finder algorithm (N-FINDR) is a simplex-based fully abundance constrained technique which is operated on the original data space. This paper presents an approach, convex-cone N-FINDR (CC N-FINDR) which combines N-FINDR with convex cone data obtained from the original data so as to improve the N-FINDR in computational complexity and performance. The same convex cone approach can be also applied to simplex growing algorithm (SGA) to derive a new convex cone-based growing algorithm (CCGA) which also improves the SGA in the same manner as it does for NFINDR. With success in CC N-FINDR and CCGA a similar treatment of using convex cone can be further used to improve any endmember extraction algorithm (EEA). Experimental results are included to demonstrate advantages of the convex cone-based EEAs over EEAs without using convex cone.

Paper Details

Date Published: 13 August 2010
PDF: 13 pages
Proc. SPIE 7812, Imaging Spectrometry XV, 78120H (13 August 2010); doi: 10.1117/12.861621
Show Author Affiliations
Wei Xiong, Univ. of Maryland, Baltimore County (United States)
Ching Tsorng Tsai, Tunghai Univ. (Taiwan)
Ching Wen Yang, Taichung Veterans General Hospital (Taiwan)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)
National Chung Hsing Univ. (Taiwan)

Published in SPIE Proceedings Vol. 7812:
Imaging Spectrometry XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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