PREDICTION METRICS FOR CHEMICAL DETECTION IN LONG-WAVE INFRARED HYPERSPECTRAL IMAGERY
Natural and man-made chemical processes generate gaseous plumes that may be detected by hyperspectral imaging, which produces a matrix of spectra affected by the chemical constituents of the plume, the atmosphere, the bounding background surface and instrument noise. A physics-based model of observed radiance shows that high chemical absorbance and low background emissivity result in a larger chemical signature. Using simulated hyperspectral imagery, this study investigated two metrics which exploited this relationship. The objective was to explore how well the chosen metrics predicted when a chemical would be more easily detected when comparing one background type to another. The two predictor metrics correctly rank ordered the backgrounds for about 94% of the chemicals tested as compared to the background rank orders from Whitened Matched Filtering (a detection algorithm) of the simulated spectra. These results suggest that the metrics provide a reasonable summary of how the background emissivity and chemical absorbance interact to produce the at-sensor chemical signal. This study suggests that similarly effective predictors that account for more general physical conditions may be derived.
- Research Organization:
- DOESC (USDOE Office of Science (SC) (United States))
- Sponsoring Organization:
- USDOE Office of Science (SC)
- OSTI ID:
- 1052117
- Journal Information:
- Journal of Undergraduate Research, Vol. 9
- Country of Publication:
- United States
- Language:
- English
Similar Records
Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery
Impact of background and atmospheric variability on infrared hyperspectral chemical detection sensitivity