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Proceedings Paper

Orthogonal projection-based fully constrained spectral unmixing
Author(s): Meiping Song; Hsiao-Chi Li; Yao Li; Cheng Gao; Chein-I Chang
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Paper Abstract

OSP has been used widely in detection and abundance estimation for about twenty years. But it can’t apply nonnegative and sum-to-one constraints when being used as an abundance estimator. Fully constrained least square algorithm does this well, but its time cost increases greatly as the number of endmembers grows. There are some tries for unmixing spectral under fully constraints from different aspects recently. Here in this paper, a new fully constrained unmixing algorithm is prompted based on orthogonal projection process, where a nearest projected point is defined onto the simplex constructed by endmembers. It is much easier, and it is faster than FCLS with the mostly same unmixing results. It is also compared with other two constrained unmixing algorithms, which shows its effectiveness too.

Paper Details

Date Published: 21 May 2015
PDF: 5 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010G (21 May 2015); doi: 10.1117/12.2177429
Show Author Affiliations
Meiping Song, Dalian Maritime Univ. (China)
Hsiao-Chi Li, Univ. of Maryland, Baltimore County (United States)
Yao Li, Univ. of Maryland, Baltimore County (United States)
Cheng Gao, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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