
Proceedings Paper
Gas plume quantification in downlooking hyperspectral longwave infrared imagesFormat | Member Price | Non-Member Price |
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Paper Abstract
Algorithms have been developed to support quantitative analysis of a gas plume using down-looking airborne
hyperspectral long-wave infrared (LWIR) imagery. The resulting gas quantification "GQ" tool estimates the quantity of
one or more gases at each pixel, and estimates uncertainty based on factors such as atmospheric transmittance,
background clutter, and plume temperature contrast. GQ uses gas-insensitive segmentation algorithms to classify the
background very precisely so that it can infer gas quantities from the differences between plume-bearing pixels and
similar non-plume pixels. It also includes MODTRAN-based algorithms to iteratively assess various profiles of air
temperature, water vapour, and ozone, and select the one that implies smooth emissivity curves for the (unknown)
materials on the ground. GQ then uses a generalized least-squares (GLS) algorithm to simultaneously estimate the most
likely mixture of background (terrain) material and foreground plume gases. Cross-linking of plume temperature to the
estimated gas quantity is very non-linear, so the GLS solution was iteratively assessed over a range of plume
temperatures to find the best fit to the observed spectrum. Quantification errors due to local variations in the camera-topixel
distance were suppressed using a subspace projection operator.
Lacking detailed depth-maps for real plumes, the GQ algorithm was tested on synthetic scenes generated by the Digital
Imaging and Remote Sensing Image Generation (DIRSIG) software. Initial results showed pixel-by-pixel gas
quantification errors of less than 15% for a Freon 134a plume.
Paper Details
Date Published: 22 October 2010
PDF: 8 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300D (22 October 2010); doi: 10.1117/12.865990
Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)
PDF: 8 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300D (22 October 2010); doi: 10.1117/12.865990
Show Author Affiliations
Caroline S. Turcotte, Defence Research and Development Canada (Canada)
Michael R. Davenport, Salience Analytics Inc. (Canada)
Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)
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