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:
-
- 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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Works referencing / citing this record:
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