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Title: Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery

Abstract

Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temper- ature, and background clutter. This paper presents an analysis of one formulation of the physics-based radiance model, which describes at-sensor observed radiance. The background emissivity and plume/ground temperatures are isolated, and their effects on net chemical signal are described. This analysis shows that the plume’s physical state, emission or absorption, is directly dependent on the background emissivity. It then describes what conditions on the background emissivity have inhibiting effects on the net chemical signal. These claims are illustrated by analyzing synthetic hyperspectral imaging data with the Adaptive Matched Filter using four chemicals and three distinct background emissivities. Two chemicals (Carbontetrachloride and Tetraflourosilane) in the analysis had a very strong relationship with the background emissivities: they exhibited absorbance over a small range of wavenumbers and the background emissivities showed a consistent ordering at these wavenumbers. Analysis of simulated hyperspectral images containing these chemicals showed complete agreement with the analysis of the physics-based model that described when the background emissivities would have inhibiting effects on gas detection. The other chemicals considered (Ammonia and Tributylphosphate) exhibited very complexmore » absorbance structure across the longwave infrared spectrum. Analysis of images containing these chemicals revealed that the the analysis of the physics-based model did not hold completely for these complex chemicals but did suggest that gas detection was dominated by their dominant absorbance features. These results provide some explanation of the effect of the background emissivity on gas detection and a more general exploration of gas absorbance/background emissivity variability and their effects on gas detection is warranted. i« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
943410
Report Number(s):
PNNL-17874
NN2001000; TRN: US200902%%178
DOE Contract Number:
AC05-76RL01830
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; 54 ENVIRONMENTAL SCIENCES; CARBON TETRACHLORIDE; SILANES; AMMONIA; TBP; DETECTION; EMISSIVITY; SIGNAL CONDITIONING; THERMAL ANALYSIS; GAS ANALYSIS

Citation Formats

Walsh, Stephen J., Tardiff, Mark F., Chilton, Lawrence K., and Metoyer, Candace N. Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery. United States: N. p., 2008. Web. doi:10.2172/943410.
Walsh, Stephen J., Tardiff, Mark F., Chilton, Lawrence K., & Metoyer, Candace N. Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery. United States. doi:10.2172/943410.
Walsh, Stephen J., Tardiff, Mark F., Chilton, Lawrence K., and Metoyer, Candace N. Thu . "Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery". United States. doi:10.2172/943410. https://www.osti.gov/servlets/purl/943410.
@article{osti_943410,
title = {Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery},
author = {Walsh, Stephen J. and Tardiff, Mark F. and Chilton, Lawrence K. and Metoyer, Candace N.},
abstractNote = {Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temper- ature, and background clutter. This paper presents an analysis of one formulation of the physics-based radiance model, which describes at-sensor observed radiance. The background emissivity and plume/ground temperatures are isolated, and their effects on net chemical signal are described. This analysis shows that the plume’s physical state, emission or absorption, is directly dependent on the background emissivity. It then describes what conditions on the background emissivity have inhibiting effects on the net chemical signal. These claims are illustrated by analyzing synthetic hyperspectral imaging data with the Adaptive Matched Filter using four chemicals and three distinct background emissivities. Two chemicals (Carbontetrachloride and Tetraflourosilane) in the analysis had a very strong relationship with the background emissivities: they exhibited absorbance over a small range of wavenumbers and the background emissivities showed a consistent ordering at these wavenumbers. Analysis of simulated hyperspectral images containing these chemicals showed complete agreement with the analysis of the physics-based model that described when the background emissivities would have inhibiting effects on gas detection. The other chemicals considered (Ammonia and Tributylphosphate) exhibited very complex absorbance structure across the longwave infrared spectrum. Analysis of images containing these chemicals revealed that the the analysis of the physics-based model did not hold completely for these complex chemicals but did suggest that gas detection was dominated by their dominant absorbance features. These results provide some explanation of the effect of the background emissivity on gas detection and a more general exploration of gas absorbance/background emissivity variability and their effects on gas detection is warranted. i},
doi = {10.2172/943410},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Oct 02 00:00:00 EDT 2008},
month = {Thu Oct 02 00:00:00 EDT 2008}
}

Technical Report:

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  • We have analyzed hyperspectral Airborne Visible-Infrared Imaging System (AVIRIS) imagery taken in September of 1992 in Long Valley Caldera, CA, a geothermally active region expressed surficially by hot springs and fumaroles. Geological and vegetation mapping are attempted through spectral classification of imagery. Particular hot spring areas in the caldera are targeted for analysis. The data is analyzed for unique geobotanical patterns in the vicinity of hot springs as well as gross identification of dominant plant and mineral species. Spectra used for the classifications come from a vegetation spectral library created for plant species found to be associated with geothermal processes.more » This library takes into account the seasonality of vegetation by including spectra for species on a monthly basis. Geological spectra are taken from JPL and USGS mineral libraries. Preliminary classifications of hot spring areas indicate some success in mineral identification and less successful vegetation species identification. The small spatial extent of individual plants demands either sub-pixel analysis or increased spatial resolution of imagery. Future work will also include preliminary analysis of a hyperspectral thermal imagery dataset and a multitemporal air photo dataset. The combination of these remotely sensed datasets for Long Valley will yield a valuable product for geothermal exploration efforts in other regions.« less
  • The detection and identification of weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temperature, and background clutter. This paper presents an analysis of one formulation of the physics-based model that describes the at-sensor observed radiance. The motivating question for the analyses performed in this paper is as follows. Given a set of backgrounds, is there a way to predict the background over which the probability of detecting a given chemical will be the highest? Two statistics were developed to address this question. These statistics incorporate data frommore » the long-wave infrared band to predict the background over which chemical detectability will be the highest. These statistics can be computed prior to data collection. As a preliminary exploration into the predictive ability of these statistics, analyses were performed on synthetic hyperspectral images. Each image contained one chemical (either carbon tetrachloride or ammonia) spread across six distinct background types. The statistics were used to generate predictions for the background ranks. Then, the predicted ranks were compared to the empirical ranks obtained from the analyses of the synthetic images. For the simplified images under consideration, the predicted and empirical ranks showed a promising amount of agreement. One statistic accurately predicted the best and worst background for detection in all of the images. Future work may include explorations of more complicated plume ingredients, background types, and noise structures.« less
  • Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temperature, and background clutter. This paper presents an analysis of one formulation of the physics-based radiance model, which describes at-sensor observed radiance. The background emissivity and plume/ground temperatures are isolated, and their effects on net chemical signal are described. This analysis shows that the plume’s physical state, emission or absorption, is directly dependent on that background emissivity. It then describes what conditions on the background emissivity have inhibiting effects on the net chemical signal. These claimsmore » are illustrated by analyzing synthetic hyperspectral imaging data with the Adaptive Matched Filter using two chemicals and three distinct background emissivities.« less