False alarm recognition in hyperspectral gas plume identification

Conger, James L; Lawson, Janice K; Aimonetti, William D

Title: False alarm recognition in hyperspectral gas plume identification

Abstract

According to one embodiment, a method for analyzing hyperspectral data includes collecting first hyperspectral data of a scene using a hyperspectral imager during a no-gas period and analyzing the first hyperspectral data using one or more gas plume detection logics. The gas plume detection logic is executed using a low detection threshold, and detects each occurrence of an observed hyperspectral signature. The method also includes generating a histogram for all occurrences of each observed hyperspectral signature which is detected using the gas plume detection logic, and determining a probability of false alarm (PFA) for all occurrences of each observed hyperspectral signature based on the histogram. Possibly at some other time, the method includes collecting second hyperspectral data, and analyzing the second hyperspectral data using the one or more gas plume detection logics and the PFA to determine if any gas is present. Other systems and methods are also included.

Inventors:

Conger, James L ^[1]; Lawson, Janice K ^[2]; Aimonetti, William D ^[3]

San Ramon, CA
Tracy, CA
Livermore, CA

Issue Date:: 2011-03-29

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1015447

Patent Number(s):: 7916947

Application Number:: US Patent Application 12/496,476

Assignee:: Lawrence Livermore National Security, LLC (Livermore, CA)

Patent Classifications (CPCs):: G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES

G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA

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G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
G01N2021/1793 - {Remote sensing}

G - PHYSICS G06 - COMPUTING G06K - RECOGNITION OF DATA
G06K9/00536 - {Classification

G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
G01N2201/1293 - resolving multicomponent spectra
G01N21/3504 - for analysing gases, e.g. multi-gas analysis

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DOE Contract Number:: AC52-07NA27344

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Conger, James L, Lawson, Janice K, and Aimonetti, William D. False alarm recognition in hyperspectral gas plume identification.  United States: N. p., 2011. 
        Web.

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                    Conger, James L, Lawson, Janice K, & Aimonetti, William D. False alarm recognition in hyperspectral gas plume identification.  United States.

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                    Conger, James L, Lawson, Janice K, and Aimonetti, William D. Tue .  
        "False alarm recognition in hyperspectral gas plume identification".  United States.  https://www.osti.gov/servlets/purl/1015447.

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@article{osti_1015447,

  title        = {False alarm recognition in hyperspectral gas plume identification},

  author       = {Conger, James L and Lawson, Janice K and Aimonetti, William D},

  abstractNote = {According to one embodiment, a method for analyzing hyperspectral data includes collecting first hyperspectral data of a scene using a hyperspectral imager during a no-gas period and analyzing the first hyperspectral data using one or more gas plume detection logics. The gas plume detection logic is executed using a low detection threshold, and detects each occurrence of an observed hyperspectral signature. The method also includes generating a histogram for all occurrences of each observed hyperspectral signature which is detected using the gas plume detection logic, and determining a probability of false alarm (PFA) for all occurrences of each observed hyperspectral signature based on the histogram. Possibly at some other time, the method includes collecting second hyperspectral data, and analyzing the second hyperspectral data using the one or more gas plume detection logics and the PFA to determine if any gas is present. Other systems and methods are also included.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2011},

  month        = {3}

}

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Works referenced in this record:

Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection
conference, May 2008

Manolakis, D.; Model, J.; Rossacci, M.
SPIE Defense and Security Symposium, SPIE Proceedings
https://doi.org/10.1117/12.775826

Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery
conference, June 2005

Theiler, James; Foy, Bernard R.; Fraser, Andrew M.
Defense and Security, SPIE Proceedings
https://doi.org/10.1117/12.604075

Detection of gas plumes in cluttered environments using long-wave infrared hyperspectral sensors
conference, May 2008

Broadwater, Joshua B.; Spisz, Thomas S.; Carr, Alison K.
SPIE Defense and Security Symposium, SPIE Proceedings
https://doi.org/10.1117/12.784707

Algorithms for chemical detection, identification and quantification for thermal hyperspectral imagers
conference, November 2005

Vallières, Alexandre; Villemaire, André; Chamberland, Martin
Optics East 2005, SPIE Proceedings
https://doi.org/10.1117/12.632114

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Similar Records

Title: False alarm recognition in hyperspectral gas plume identification

Abstract

Citation Formats

Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection conference, May 2008

Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery conference, June 2005

Detection of gas plumes in cluttered environments using long-wave infrared hyperspectral sensors conference, May 2008

Algorithms for chemical detection, identification and quantification for thermal hyperspectral imagers conference, November 2005

Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection
conference, May 2008

Characterizing non-Gaussian clutter and detecting weak gaseous plumes in hyperspectral imagery
conference, June 2005

Detection of gas plumes in cluttered environments using long-wave infrared hyperspectral sensors
conference, May 2008

Algorithms for chemical detection, identification and quantification for thermal hyperspectral imagers
conference, November 2005