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• W2056333885 abstract "A novel approach for quantification of chemical vapor effluents in stack plumes using infrared hyperspectral imaging are presented and examined. The algorithms use a novel application of the extended mixture model to provide estimates of background clutter in the on-plume pixel. These estimates are then used iteratively to improve the quantification. The final step in the algorithm employs either an extended least-squares (ELS) or generalized least-squares (GLS) procedure. It was found that the GLS weighting procedure generally performed better than ELS, but they performed similarly when the analyte spectra had relatively narrow features. The algorithms require estimates of the atmospheric radiance and transmission from the target plume to the imaging spectrometer and an estimate of the plume temperature. However, estimates of the background temperature and emissivity are not required which is a distinct advantage. The algorithm effectively provides a local estimate of the clutter, and an error analysis shows that it can provide superior quantification over approaches that model the background clutter in a more global sense. It was also found that the estimation error depended strongly on the net analyte signal for each analyte, and this quantity is scenario-specific." @default.
• W2056333885 created "2016-06-24" @default.
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• W2056333885 date "2003-08-01" @default.
• W2056333885 modified "2023-10-14" @default.
• W2056333885 title "Estimation of trace vapor concentration-pathlength in plumes for remote sensing applications from hyperspectral images" @default.
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• W2056333885 doi "https://doi.org/10.1016/s0003-2670(03)00177-6" @default.
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