Overhead longwave infrared hyperspectral material identification using radiometric models

doi:10.1117/1.JRS.12.025019

Title: Overhead longwave infrared hyperspectral material identification using radiometric models

Abstract

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help mitigate false positives. A material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms is presented. The algorithms utilize unwhitened radiance data and Nelder–Meade numerical optimization to estimate the temperature, humidity, and ozone levels of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian information criteria for model selection. The resulting identification product includes an optimal atmospheric profile and a full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing the model parameters to improve identification is also presented. Furthermore, several examples are provided using modeled data at several noise levels.

Authors:

Zelinski, Michael E. ^[1]

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Publication Date:: 2018-06-30

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

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1474374

Report Number(s):: LLNL-JRNL-758262
Journal ID: ISSN 1931-3195; 943925

Grant/Contract Number:: AC52-07NA27344

Resource Type:: Accepted Manuscript

Journal Name:: Jounral of Applied Remote Sensing

Additional Journal Information:: Journal Volume: 12; Journal Issue: 02; Journal ID: ISSN 1931-3195

Publisher:: SPIE

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; hyperspectral; longwave infrared; radiometric modeling; detection; identification

Citation Formats


                    Zelinski, Michael E. Overhead longwave infrared hyperspectral material identification using radiometric models.  United States: N. p., 2018. 
        Web.  doi:10.1117/1.JRS.12.025019.

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                    Zelinski, Michael E. Overhead longwave infrared hyperspectral material identification using radiometric models.  United States.  doi:10.1117/1.JRS.12.025019.

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                    Zelinski, Michael E. Sat .  
        "Overhead longwave infrared hyperspectral material identification using radiometric models".  United States.  doi:10.1117/1.JRS.12.025019.  https://www.osti.gov/servlets/purl/1474374.

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@article{osti_1474374,

  title        = {Overhead longwave infrared hyperspectral material identification using radiometric models},

  author       = {Zelinski, Michael E.},

  abstractNote = {Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help mitigate false positives. A material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms is presented. The algorithms utilize unwhitened radiance data and Nelder–Meade numerical optimization to estimate the temperature, humidity, and ozone levels of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian information criteria for model selection. The resulting identification product includes an optimal atmospheric profile and a full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing the model parameters to improve identification is also presented. Furthermore, several examples are provided using modeled data at several noise levels.},

  doi          = {10.1117/1.JRS.12.025019},

  journal      = {Jounral of Applied Remote Sensing},

  number       = 02,

  volume       = 12,

  place        = {United States},

  year         = {2018},

  month        = {6}

}

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Journal Article:

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DOI: 10.1117/1.JRS.12.025019

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Works referenced in this record:

A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels
journal, April 2015

Cubero-Castan, Manuel; Chanussot, Jocelyn; Achard, Veronique
IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, Issue 4
DOI: 10.1109/TGRS.2014.2350771

Estimation of trace vapor concentration-pathlength in plumes for remote sensing applications from hyperspectral images
journal, August 2003

Gallagher, Neal B.; Wise, Barry M.; Sheen, David M.
Analytica Chimica Acta, Vol. 490, Issue 1-2
DOI: 10.1016/S0003-2670(03)00177-6

An in-scene method for atmospheric compensation of thermal hyperspectral data
journal, January 2002

Young, Stephen J.
Journal of Geophysical Research, Vol. 107, Issue D24
DOI: 10.1029/2001JD001266

Taxonomy of detection algorithms for hyperspectral imaging applications
journal, June 2005

Manolakis, Dimitris
Optical Engineering, Vol. 44, Issue 6
DOI: 10.1117/1.1930927

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Title: Overhead longwave infrared hyperspectral material identification using radiometric models

Abstract

Citation Formats

A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels journal, April 2015

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An in-scene method for atmospheric compensation of thermal hyperspectral data journal, January 2002

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journal, August 2003

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