Prediction Metrics for Chemical Detection in Long-Wave Infrared Hyperspectral Imagery
A natural or anthropogenic process often generates a signature gas plume whose chemical constituents may be identified using hyperspectral imagery. A hyperspectral image is a pixel-indexed set of spectra where each spectrum reflects the chemical constituents of the plume, the atmosphere, the bounding background surface, and instrument noise. This study explored the relationship between gas absorbance and background emissivity across the long-wave infrared (LWIR) spectrum and how they affect relative gas detection sensitivity. The physics-based model for the observed radiance shows that high gas absorbance coupled with low background emissivity at a single wavenumber results in a stronger recorded radiance. Two sensitivity measures were developed to predict relative probability of detection using chemical absorbance and background emissivity: one focused on a single wavenumber while another accounted for the entire spectrum. The predictive abilities of these measures were compared to synthetic image analysis. This study simulated images with 499 distinct gases at each of 6 concentrations over 6 different background surfaces with the atmosphere and level of instrument noise held constant. The Whitened Matched Filter was used to define gas detection from an image spectrum. The estimate of a chemical’s probability of detection at a given concentration over a specific background was the proportion of detections in 500 trials. Of the 499 chemicals used in the images, 276 had estimated probabilities of detection below 0.2 across all backgrounds and concentrations; these chemicals were removed from the study. For 92.8 percent of the remaining chemicals, the single channel measure correctly predicted the background over which the chemical had the largest relative probability of detection. Further, the measure which accounted for information across all wavenumbers predicted the background over which the chemical had the largest relative probability of detection for 93.3 percent of the chemicals. These results suggest that the wavenumber with largest gas absorbance has the most influence over gas detection for this data. By furthering the in-silico experimentation with higher concentrations of gases not detectable in this experiment or by standardizing the gas absorbance spectra to unit vectors, these conclusions may be confirmed and generalized to more gases. This will help simplify image acquisition planning and the identification of unknowns in field collected images.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 993351
- Report Number(s):
- PNNL-SA-62000; NN2001000; TRN: US201023%%217
- Journal Information:
- Journal of Undergraduate Research, IX:48-52, Vol. 9
- Country of Publication:
- United States
- Language:
- English
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PREDICTION METRICS FOR CHEMICAL DETECTION IN LONG-WAVE INFRARED HYPERSPECTRAL IMAGERY