Paper
11 April 2008 Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection
D. Manolakis, J. Model, M. Rossacci, D. Zhang, E. Ontiveros, M. Pieper, J. Seeley, D. Weitz
Author Affiliations +
Abstract
The long-wave infrared (LWIR) hyperpectral sensing modality is one that is often used for the problem of detection and identification of chemical warfare agents (CWA) which apply to both military and civilian situations. The inherent nature and complexity of background clutter dictates a need for sophisticated and robust statistical models which are then used in the design of optimum signal processing algorithms that then provide the best exploitation of hyperspectral data to ultimately make decisions on the absence or presence of potentially harmful CWAs. This paper describes the basic elements of an automated signal processing pipeline developed at MIT Lincoln Laboratory. In addition to describing this signal processing architecture in detail, we briefly describe the key signal models that form the foundation of these algorithms as well as some spatial processing techniques used for false alarm mitigation. Finally, we apply this processing pipeline to real data measured by the Telops FIRST hyperspectral (FIRST) sensor to demonstrate its practical utility for the user community.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
D. Manolakis, J. Model, M. Rossacci, D. Zhang, E. Ontiveros, M. Pieper, J. Seeley, and D. Weitz "Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69661U (11 April 2008); https://doi.org/10.1117/12.775826
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Cited by 2 scholarly publications.
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KEYWORDS
Sensors

Signal processing

Long wavelength infrared

Statistical analysis

Binary data

Detection and tracking algorithms

Algorithm development

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