5 November 2005 Spectral quality requirements for effluent identification
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Proceedings Volume 5995, Chemical and Biological Standoff Detection III; 599504 (2005); doi: 10.1117/12.631311
Event: Optics East 2005, 2005, Boston, MA, United States
Abstract
We consider the problem of remotely identifying gaseous materials using passive sensing of long-wave infrared (LWIR) spectral features at hyperspectral resolution. Gaseous materials are distinguishable in the LWIR because of their unique spectral fingerprints. A sensor degraded in capability by noise or limited spectral resolution, however, may be unable to positively identify contaminants, especially if they are present in low concentrations or if the spectral library used for comparisons includes materials with similar spectral signatures. This paper will quantify the relative importance of these parameters and express the relationships between them in a functional form which can be used as a rule of thumb in sensor design or in assessing sensor capability for a specific task. This paper describes the simulation of remote sensing datacontaining a gas cloud.In each simulation, the spectra are degraded in spectral resolution and through the addition of noise to simulate spectra collected by sensors of varying design and capability. We form a trade space by systematically varying the number of sensor spectral channels and signal-to-noise ratio over a range of values. For each scenario, we evaluate the capability of the sensor for gas identification by computing the ratio of the F-statistic for the truth gas tothe same statistic computed over the rest of the library.The effect of the scope of the library is investigated as well, by computing statistics on the variability of the identification capability as the library composition is varied randomly.
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R. N. Czerwinski, J. A. Seeley, E. C. Wack, "Spectral quality requirements for effluent identification", Proc. SPIE 5995, Chemical and Biological Standoff Detection III, 599504 (5 November 2005); doi: 10.1117/12.631311; https://doi.org/10.1117/12.631311
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KEYWORDS
Sensors

Signal to noise ratio

Gases

Long wavelength infrared

Spectral resolution

Remote sensing

Imaging systems

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