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7 May 2007 Statistical models for LWIR hyperspectral backgrounds and their applications in chemical agent detection
D. Manolakis; L. G. Jairam; D. Zhang; M. Rossacci
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Proceedings Volume 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII; 656525 (2007) https://doi.org/10.1117/12.718378
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States
Abstract
Remote detection of chemical vapors in the atmosphere has a wide range of civilian and military applications. In the past few years there has been significant interest in the detection of effluent plumes using hyperspectral imaging spectroscopy in the 8-13&mgr;m atmospheric window. A major obstacle in the full exploitation of this technology is the fact that everything in the infrared is a source of radiation. As a result, the emission from the gases of interest is always mixed with emission by the more abundant atmospheric constituents and by other objects in the sensor field of view. The radiance fluctuations in this background emission constitute an additional source of interference which is much stronger than the detector noise. In this paper we develop and evaluate parametric models for the statistical characterization of LWIR hyperspectral backgrounds. We consider models based on the theory of elliptically contoured distributions. Both models can handle heavy tails, which is a key stastical feature of hyperspectral imaging backgrounds. The paper provides a concise description of the underlying models, the algorithms used to estimate their parameters from the background spectral measurements, and the use of the developed models in the design and evaluation of chemical warfare agent detection algorithms.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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D. Manolakis, L. G. Jairam, D. Zhang, and M. Rossacci "Statistical models for LWIR hyperspectral backgrounds and their applications in chemical agent detection", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656525 (7 May 2007); https://doi.org/10.1117/12.718378
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