Passive Fourier transform infrared (FT-IR) remote sensing measurements are used to implement the automated detection of trichloroethylene (TCE) vapor in the presence of a variety of infrared background signatures. Through the use of a combination of bandpass digital filtering and piecewise linear discriminant analysis, this detection procedure is applied directly to short segments of the interferogram data collected by the FT-IR spectrometer. Data employed in this work were collected during openair/passive cell terrestrial and passive cell laboratory measurements. Infrared backgrounds employed included terrain, low-angle sky, and water backgrounds, in addition to laboratory blackbody measurements. Other potentially interfering chemical species present were carbon tetrachloride, acetone, methyl ethyl ketone, and sulfur hexafluoride (SF 6 ). These data are used to assemble two data sets of differing complexity. Optimization studies are performed separately with each data set to study the influence of filter bandpass position, bandpass width, interferogram segment location, and segment size on the ability to detect TCE.The optimal parameters found consist of a Gaussian-shaped filter positioned at 939.5 cm -1 , with a width at half-height of 123.4 cm -1 . This filter is applied to interferogram points 111-220 (relative to the centerburst). When applied to a prediction set of 60 000 interferograms, the piecewise linear discriminant developed on the basis of these optimal parameters is found to detect TCE successfully in 96.2% of the cases in which it is present. The overall rate of false detections is 0.5%. The limit of detection of TCE is found to be 102 ppm-m at a temperature difference of 10.5 °C between the infrared background and the analyte. SF 6 is observed to provide the greatest spectral interference among the compounds tested, producing a false detection rate of 8.6%. It is found that this false detection rate can be reduced to 1.5% through the development of a probabilitybased interpretation of the piecewise linear discriminant results. These results are observed to compare favorably with those obtained in a separate analysis of filtered singlebeam spectra.

Methodology is developed for the automated detection of heated plumes of ethanol vapor with airborne passive Fourier transform infrared spectrometry. Positioned in a fixed-wing aircraft in a downward-looking mode, the spectrometer is used to detect ground sources of ethanol vapor from an altitude of 2000-3000 ft. Challenges to the use of this approach for the routine detection of chemical plumes include (1) the presence of a constantly changing background radiance as the aircraft flies, (2) the cost and complexity of collecting the data needed to train the classification algorithms used in implementing the plume detection, and (3) the need for rapid interferogram scans to minimize the ground area viewed per scan. To address these challenges, this work couples a novel ground-based data collection and training protocol with the use of signal processing and pattern recognition methods based on short sections of the interferogram data collected by the spectrometer. In the data collection, heated plumes of ethanol vapor are released from a portable emission stack and viewed by the spectrometer from ground level against a synthetic background designed to simulate a terrestrial radiance source. Classifiers trained with these data are subsequently tested with airborne data collected over a period of 2.5 years. Two classifier architectures are compared in this work: support vector machines (SVM) and piecewise linear discriminant analysis (PLDA). When applied to the airborne test data, the SVM classifiers perform best, failing to detect ethanol in only 8% of the cases in which it is present. False detections occur at a rate of less than 0.5%. The classifier performs well in spite of differences between the backgrounds associated with the ground-based and airborne data collections and the instrumental drift arising from the long time span of the data collection. Further improvements in classification performance are judged to require increased sophistication in the ground-based data collection in order to provide a better match to the infrared backgrounds observed from the air.

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