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