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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:
 [1]
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  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Publication Date:
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:
Journal 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 = {Journal of Applied Remote Sensing},
number = 02,
volume = 12,
place = {United States},
year = {2018},
month = {6}
}
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DOI:  10.1117/1.JRS.12.025019
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Works referenced in this record:

Recent advances in temperature-emissivity separation algorithms
conference, March 2011

  • Borel, Christoph C.; Tuttle, Ronald F.
  • 2011 IEEE Aerospace Conference, 2011 Aerospace Conference
  • DOI: 10.1109/AERO.2011.5747397

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

A detection-identification process with geometric target detection and subpixel spectral visualization
conference, June 2011

  • Basener, Bill; Schlamm, Ariel; Messinger, David
  • 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
  • DOI: 10.1109/WHISPERS.2011.6080948

HyTES: Thermal imaging spectrometer development
conference, March 2011

  • Johnson, William R.; Hook, Simon J.; Mouroulis, Pantazis
  • 2011 IEEE Aerospace Conference, 2011 Aerospace Conference
  • DOI: 10.1109/AERO.2011.5747394

Hyperspectral sub-pixel target identification using least-angle regression
conference, April 2010

  • Villeneuve, Pierre V.; Boisvert, Alex R.; Stocker, Alan D.
  • SPIE Defense, Security, and Sensing, SPIE Proceedings
  • DOI: 10.1117/12.850563

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

Development of a Web-Based Application to Evaluate Target Finding Algorithms
conference, July 2008

  • Snyder, D.; Kerekes, J.; Fairweather, I.
  • IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
  • DOI: 10.1109/IGARSS.2008.4779144

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

LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing
conference, November 1996

  • Hackwell, John A.; Warren, David W.; Bongiovi, Robert P.
  • SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, SPIE Proceedings
  • DOI: 10.1117/12.258057

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

Surface emissivity and temperature retrieval for a hyperspectral sensor
conference, January 1998

  • Borel, C. C.
  • IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)
  • DOI: 10.1109/IGARSS.1998.702966
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    A new method of relative radiometric calibration for hyperspectral imaging based on skylight monitor
    journal, November 2019

    • Zhou, Shi-yao; Zhang, Dong; Liu, Hong-lin
    • Optical and Quantum Electronics, Vol. 51, Issue 11
    • DOI: 10.1007/s11082-019-2092-5
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