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Proceedings Paper

A fast full constraints unmixing method
Author(s): Zhang Ye; Ran Wei; Qing Yan Wang
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Paper Abstract

Mixed pixels are inevitable due to low-spatial resolutions of hyperspectral image (HSI). Linear spectrum mixture model (LSMM) is a classical mathematical model to relate the spectrum of mixing substance to corresponding individual components. The solving of LSMM, namely unmixing, is essentially a linear optimization problem with constraints, which is usually consisting of iterations implemented on decent direction and stopping criterion to terminate algorithms. Such criterion must be properly set in order to balance the accuracy and speed of solution. However, the criterion in existing algorithm is too strict, which maybe lead to convergence rate reducing. In this paper, by broaden constraints in unmixing, a new stopping rule is proposed, which can reduce rate of convergence. The experiments results prove both in runtime and iteration numbers that our method can accelerate convergence processing with only cost of little quality decrease in resulting.

Paper Details

Date Published: 19 October 2012
PDF: 7 pages
Proc. SPIE 8514, Satellite Data Compression, Communications, and Processing VIII, 85140I (19 October 2012); doi: 10.1117/12.961010
Show Author Affiliations
Zhang Ye, Harbin Institute of Technology (China)
Ran Wei, Harbin Institute of Technology (China)
Qing Yan Wang, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 8514:
Satellite Data Compression, Communications, and Processing VIII
Bormin Huang; Antonio J. Plaza; Carole Thiebaut, Editor(s)

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