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Proceedings Paper

The invariant algorithm for identification and detection of multiple gas plumes and weak releases
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Paper Abstract

The ability to detect and identify gaseous effluents is a problem that has been pursued with limited success. It has been shown to be possible using the Invariant algorithm on synthetic hyperspectral scenes with a strong single gas release. That however, is a very specific case and leaves room for further investigation. This study looks at more realistic detection and release scenarios. Our implementation of the Invariant algorithm uses Singular Value Decomposition (SVD) to select basis vectors from a subspace of target gases in conjunction with a Generalized Likelihood Ratio Test (GLRT) to determine on a pixel by pixel basis how ``like" the target gas each pixel is. The target gases are modeled in the image radiance space including atmospheric effects. Target spectra are modeled in both emission and absorption. This study investigates how well weak plumes are detected. Also, there will be a test of a mixed gas in a strong plume release. Finally, a situation where a weak multiple gas release will be discussed. Synthetic hyperspectral imagery in the long wave infrared region (LWIR) of the electromagnetic spectrum will be the predominate data used in this study. This algorithm has been found to be applicable for these detection and identification scenarios.

Paper Details

Date Published: 1 June 2005
PDF: 12 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.603940
Show Author Affiliations
Erin M. O'Donnell, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
Carl Salvaggio, Rochester Institute of Technology (United States)
John R. Schott, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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