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Detection of Gaseous Plumes using Basis Vectors.

Chilton L, Walsh S - Sensors (Basel) (2009)

Bottom Line: These include principle components, independent components, entropy, Fourier transform, and others.These methods do not explicitly take advantage of the physics of the signal formulation process and therefore don't exploit all available information in the data.This paper describes generalized least squares detection using gas spectra, presents a new detection method using basis vectors, and compares detection images resulting from applying both methods to synthetic hyperspectral data.

View Article: PubMed Central - PubMed

Affiliation: PO Box 999, Pacific Northwest National Laboratory, Richland, WA 99352 E-Mail: stephen.walsh@pnl.gov.

ABSTRACT
Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. There are several methods currently being used to detect plumes. They can be grouped into two categories: those that use a chemical spectral library and those that don't. The approaches that use chemical libraries include physics-based least squares methods (matched filter). They are "optimal" only if the plume chemical is actually in the search library but risk missing chemicals not in the library. The methods that don't use a chemical spectral library are based on a statistical or data analytical transformation applied to the data. These include principle components, independent components, entropy, Fourier transform, and others. These methods do not explicitly take advantage of the physics of the signal formulation process and therefore don't exploit all available information in the data. This paper describes generalized least squares detection using gas spectra, presents a new detection method using basis vectors, and compares detection images resulting from applying both methods to synthetic hyperspectral data.

No MeSH data available.


Related in: MedlinePlus

Images that show (a) a wideband picture of the synthetic DIRSIG image and (b) a mask image of the gaussian shaped NH3 (lower left) and Freon (upper right) plumes.
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f1-sensors-09-03205: Images that show (a) a wideband picture of the synthetic DIRSIG image and (b) a mask image of the gaussian shaped NH3 (lower left) and Freon (upper right) plumes.

Mentions: The DIRSIG image represents a highly cluttered urban scene. The image has 200 × 200 spatial pixels with 128 LWIR channels ranging from 7.5188 μm to 13.605 μm. This image includes two large simulated plume releases, one each for gases Freon-114 and ammonia (NH3). A wideband picture of the DIRSIG image and the plume mask are presented in Figure 1(a) and (b) respectively. The plume temperature and concentration path-lengths are strongest near the release point. The plume concentration path-lengths vary from approximately 70 ppm-m at the source to approximately 1 ppm-m at the lower right edge of the image. Figure 1(b) shows that the plumes cover a considerable region of the synthetic scene. We note that the two large plumes cover approximately 23% of the image.


Detection of Gaseous Plumes using Basis Vectors.

Chilton L, Walsh S - Sensors (Basel) (2009)

Images that show (a) a wideband picture of the synthetic DIRSIG image and (b) a mask image of the gaussian shaped NH3 (lower left) and Freon (upper right) plumes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3297141&req=5

f1-sensors-09-03205: Images that show (a) a wideband picture of the synthetic DIRSIG image and (b) a mask image of the gaussian shaped NH3 (lower left) and Freon (upper right) plumes.
Mentions: The DIRSIG image represents a highly cluttered urban scene. The image has 200 × 200 spatial pixels with 128 LWIR channels ranging from 7.5188 μm to 13.605 μm. This image includes two large simulated plume releases, one each for gases Freon-114 and ammonia (NH3). A wideband picture of the DIRSIG image and the plume mask are presented in Figure 1(a) and (b) respectively. The plume temperature and concentration path-lengths are strongest near the release point. The plume concentration path-lengths vary from approximately 70 ppm-m at the source to approximately 1 ppm-m at the lower right edge of the image. Figure 1(b) shows that the plumes cover a considerable region of the synthetic scene. We note that the two large plumes cover approximately 23% of the image.

Bottom Line: These include principle components, independent components, entropy, Fourier transform, and others.These methods do not explicitly take advantage of the physics of the signal formulation process and therefore don't exploit all available information in the data.This paper describes generalized least squares detection using gas spectra, presents a new detection method using basis vectors, and compares detection images resulting from applying both methods to synthetic hyperspectral data.

View Article: PubMed Central - PubMed

Affiliation: PO Box 999, Pacific Northwest National Laboratory, Richland, WA 99352 E-Mail: stephen.walsh@pnl.gov.

ABSTRACT
Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. There are several methods currently being used to detect plumes. They can be grouped into two categories: those that use a chemical spectral library and those that don't. The approaches that use chemical libraries include physics-based least squares methods (matched filter). They are "optimal" only if the plume chemical is actually in the search library but risk missing chemicals not in the library. The methods that don't use a chemical spectral library are based on a statistical or data analytical transformation applied to the data. These include principle components, independent components, entropy, Fourier transform, and others. These methods do not explicitly take advantage of the physics of the signal formulation process and therefore don't exploit all available information in the data. This paper describes generalized least squares detection using gas spectra, presents a new detection method using basis vectors, and compares detection images resulting from applying both methods to synthetic hyperspectral data.

No MeSH data available.


Related in: MedlinePlus