@inproceedings{020f5d8d415946e7a85fc203d08b99a9,
title = "Detecting plumes in LWIR using robust nonnegative matrix factorization with graph-based initialization",
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 Nystr{\"o}m 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.",
keywords = "Data analysis, Hyperspectral images, Image processing, Label propagation, Non-negative matrix factorization, Nystr{\"o}m extension, Robust principal component analysis, Spectral clustering",
author = "Jing Qin and Thomas Laurent and Kevin Bui and Tan, {Ricardo Vicente R.} and Jasmine Dahilig and Shuyi Wang and Jared Rohe and Justin Sunu and Bertozzi, {Andrea L.}",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; null ; Conference date: 21-04-2015 Through 23-04-2015",
year = "2015",
doi = "10.1117/12.2177342",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
editor = "Miguel Velez-Reyes and Kruse, {Fred A.}",
booktitle = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI",
}