Detection of gaseous effluents from airborne LWIR hyperspectral imagery using physics-based signatures

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dc.contributor.author Messinger, David en_US
dc.contributor.author Salvaggio, Carl en_US
dc.contributor.author Sinisgalli, Natalie en_US
dc.date.accessioned 2007-06-22T15:00:45Z en_US
dc.date.available 2007-06-22T15:00:45Z en_US
dc.date.issued 2006-07 en_US
dc.identifier.citation International Journal of High Speed Electronics and Systems en_US
dc.identifier.issn 0129-1564 en_US
dc.identifier.uri http://hdl.handle.net/1850/4039 en_US
dc.description Submitted for publication in IEEE International Journal of High Speed Electronics and Systems, July 2006. en_US
dc.description RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/
dc.description.abstract Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration pathlength and temperature contrast, nor does it take into affect measured signatures that are observed in both absorption and emission in the same scene. We develop a physics-based, forward model to predict in-scene signatures covering a wide range in gas / surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physicsbased signature approach shows significantly improved performance for the data set considered here. en_US
dc.description.sponsorship The authors would like to thank Mr. David Williams of the US Environmental Protection Agency for providing the data used in this study. en_US
dc.language.iso en_US en_US
dc.publisher World Scientific en_US
dc.subject Detection en_US
dc.subject Gaseous effluent en_US
dc.subject Hyperspectral en_US
dc.subject Longwave infrared en_US
dc.title Detection of gaseous effluents from airborne LWIR hyperspectral imagery using physics-based signatures en_US
dc.type Preprint en_US

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