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Proceedings Paper

Endmember extraction in hyperspectral images using l-1 minimization and linear complementary programming
Author(s): Dzung Nguyen; Trac Tran; Chiman Kwan; Bulent Ayhan
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Paper Abstract

Endmember extraction in Hyperspectral Images (HSI) is a critical step for target detection and abundance estimation. In this paper, we propose a new approach to endmember extraction, which takes advantage of the sparsity property of the linear representation of HSI's spectral vector. Sparsity is measured by the l0 norm of the abundance vector. It is also well known that l1 norm well resembles l0 in boosting sparsity while keeping the minimization problem convex and tractable. By adding the l1 norm term to the objective function, we result in a constrained quadratic programming which can be solved effectively using the Linear Complementary Programming (LCP). Unlike existing methods which require expensive computations in each iteration, LCP only requires pivoting steps, which are extremely simple and efficient for the un-mixing problem, since the number of signatures in the reconstructing basis is reasonably small. Preliminary experiments of the proposed methods for both supervised and unsupervised abundance decomposition showed competitive results as compared to LS-based method like Fully Constrained Least Square (FCLS). Furthermore, combination of our unsupervised decomposition with anomaly detection makes a decent target detection algorithm as compared to methods which require prior information of target and background signatures.

Paper Details

Date Published: 12 May 2010
PDF: 9 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951M (12 May 2010); doi: 10.1117/12.850139
Show Author Affiliations
Dzung Nguyen, The Johns Hopkins Univ. (United States)
Trac Tran, The Johns Hopkins Univ. (United States)
Chiman Kwan, Signal Processing, Inc. (United States)
Bulent Ayhan, Signal Processing, Inc. (United States)

Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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