Automated pattern recognition methodology is described for the detection of signatures of volatile organic compounds from passive multispectral infrared imaging data collected from an aircraft platform. Data are acquired in an across-track scanning mode with a downward-looking line scanner based on 8 to 16 spectral channels in the 8-14 and 3-5 microm spectral ranges. Two controlled release experiments are performed in which plumes of ethanol are generated and detected from aircraft overflights at altitudes of 2200 to 2800 ft (671 to 853 m). In addition, a methanol release from a chemical manufacturing facility is monitored. Automated classifiers are developed by application of piecewise linear discriminant analysis to the calibrated, registered, and preprocessed radiance data acquired by the line scanner. Preprocessing steps evaluated include contrast enhancement, temperature-emissivity separation, feature selection, and feature extraction/noise reduction by the minimum noise fraction (MNF) transform. Successful classifiers are developed for both compounds and are tested with data not used in the classifier development. Separation of temperature and emissivity by use of the alpha residual calculation is found to reduce false positive detections to a negligible level, and the MNF transform is shown to enhance detection sensitivity.
An automated classification algorithm is implemented for the detection of ammonia vapor in heated plumes by passive Fourier transform infrared (FT-IR) spectrometry. This classification methodology allows the real-time detection of chemical signatures in gaseous effluents such as those generated from industrial processes. The characteristics of real-time implementation and excellent robustness are achieved by an analysis strategy based on the application of band-pass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer, followed by the use of piecewise linear discriminant analysis to obtain a yes/no classification regarding the presence of the analyte signature in the filtered data. The optimal classifier developed through this work is based on only 110 interferogram points and employs a single band-pass filter centered at 945 cm(-)(1) with a pass-band full width at half-maximum of 93 cm(-)(1). The average stop-band attenuation of the optimal filter is 42.1 dB. The robustness of the algorithm is tested by exposing it to chemical releases of sulfur hexafluoride, ethanol, methanol, sulfur dioxide, and hydrogen chloride that were not included in the development of the classifier. Excellent classification performance is demonstrated, with missed ammonia detections occurring at a rate of approximately 1%. The occurrence of false detections is less than 0.1% for SF(6) and less than 0.02% for the other interferences tested.
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