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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 separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.

Authors:
 [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1417957
Report Number(s):
LLNL-TR-744504
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; Hyperspectral; LWIR; radiometric modeling; detection; identification

Citation Formats

Zelinski, M. E.. Overhead longwave infrared hyperspectral material identification using radiometric models. United States: N. p., 2018. Web. doi:10.2172/1417957.
Zelinski, M. E.. Overhead longwave infrared hyperspectral material identification using radiometric models. United States. doi:10.2172/1417957.
Zelinski, M. E.. Tue . "Overhead longwave infrared hyperspectral material identification using radiometric models". United States. doi:10.2172/1417957. https://www.osti.gov/servlets/purl/1417957.
@article{osti_1417957,
title = {Overhead longwave infrared hyperspectral material identification using radiometric models},
author = {Zelinski, M. 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 separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.},
doi = {10.2172/1417957},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 09 00:00:00 EST 2018},
month = {Tue Jan 09 00:00:00 EST 2018}
}

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