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Proceedings Paper

Detecting plumes in LWIR using robust nonnegative matrix factorization with graph-based initialization
Author(s): Jing Qin; Thomas Laurent; Kevin Bui; Ricardo V. R. Tan; Jasmine Dahilig; Shuyi Wang; Jared Rohe; Justin Sunu; Andrea L. Bertozzi
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Paper Abstract

We consider the problem of identifying chemical plumes in hyperspectral imaging data, which is challenging due to the diffusivity of plumes and the presence of excessive noise. We propose a robust nonnegative matrix factorization (RNMF) method to segment hyperspectral images considering the low-rank structure of the noisefree data and sparsity of the noise. Because the optimization objective is highly non-convex, nonnegative matrix factorization is very sensitive to initialization. We address the issue by using the fast Nystrom method and label propagation algorithm (LPA). Using the alternating direction method of multipliers (ADMM), RNMF provides high quality clustering results effectively. Experimental results on real single frame and multiframe hyperspectral data with chemical plumes show that the proposed approach is promising in terms of clustering quality and detection accuracy.

Paper Details

Date Published: 21 May 2015
PDF: 11 pages
Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94720V (21 May 2015); doi: 10.1117/12.2177342
Show Author Affiliations
Jing Qin, Univ. of California, Los Angeles (United States)
Thomas Laurent, Loyola Marymount Univ. (United States)
Kevin Bui, Univ. of California, Los Angeles (United States)
Ricardo V. R. Tan, Loyola Marymount Univ. (United States)
Jasmine Dahilig, Loyola Marymount Univ. (United States)
Shuyi Wang, Univ. of California, Los Angeles (United States)
Jared Rohe, Univ. of San Francisco (United States)
Justin Sunu, Claremont Graduate Univ. (United States)
Andrea L. Bertozzi, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 9472:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)

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