Rapid signal processing techniques for Fourier transform infrared remote sensing

Small, Gary W.; Kaltenbach, Thomas F.; Kroutil, Robert T.

doi:10.1016/0165-9936(91)85117-a

Cited by 31 publications

(3 citation statements)

References 11 publications

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“…In recent literature, background subtraction methods have been shown to be of limited use for passive infrared spectral measurements. 5,6 Detection of gases is complicated by the fact that the absorptivities of a molecule may be low, and the change in radiant energy impinging on the sensor varies significantly. In many openpath passive experiments, it has been very difficult to obtain accurate background reference spectra.…”

Section: Infrared Interferogram Analysis Of Passive Ft-ir Datamentioning

confidence: 99%

Remote-sensing and in-situ atmospheric chemistry studies with the use of a manned hot air balloon platform

Watson

Kroutil

Traynor

et al. 1996

Field Analyt. Chem. Technol.

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Section: Infrared Interferogram Analysis Of Passive Ft-ir Datamentioning

confidence: 99%

Remote-sensing and in-situ atmospheric chemistry studies with the use of a manned hot air balloon platform

Watson

Kroutil

Traynor

et al. 1996

Field Analyt. Chem. Technol.

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“…These considerations dictate that an ideal data analysis algorithm for use with passive FT-IR remote sensing data must be insensitive to changes in the background radiance and must facilitate rapid data collection. To address both issues, our laboratory has developed an analysis strategy based on the use of short segments of the interferogram data collected by the FT-IR spectrometer. − By optimal selection of the interferogram segment location, a significant amount of information representing the infrared background can be removed. Application of band-pass digital filters to the selected segment then allows extraction of analyte information on the basis of characteristic spectral frequencies.…”

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confidence: 99%

Artificial Neural Networks for the Automated Detection of Trichloroethylene by Passive Fourier Transform Infrared Spectrometry

Hammer¹,

Small²,

Combs³

et al. 2000

Anal. Chem.

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Artificial neural networks are applied to the automated classification of trichloroethylene (TCE) signatures from passive Fourier transform infrared remote sensing interferogram data. Through the use of three data collection methods, a combination of laboratory and field data is acquired that allows the methodology to be evaluated under a variety of infrared background conditions and in the presence of potentially interfering compounds such as sulfur hexafluoride, methyl ethyl ketone, acetone, carbon tetrachloride, and ammonia. To maximize the computational efficiency of the network optimization, experimental design techniques are employed to develop a training protocol for the network that takes into account the relationships among five variables that are related either to the network architecture or to the training process. This protocol is implemented for the case of a back-propagation neural network (BNN) and is used to develop an optimized network for the detection of TCE. The classification performance of the network is assessed by comparing both TCE detection capabilities and false detection rates to similar classification results obtained with the technique of piecewise linear discriminant analysis (PLDA). When applied to prediction data withheld from the optimization of both the BNN and PLDA algorithms, the BNN method is observed to outperform PLDA overall, with TCE detection rates in excess of 99% and false detection rates less than 0.5%.

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“…Research in our laboratory is focused on overcoming this limitation by use of novel data analysis strategies for suppressing the contributions of the infrared background without performing an actual background measurement. − This methodology is based on the direct analysis of short segments of FT-IR interferogram data and combines bandpass digital filtering and pattern recognition techniques to achieve the detection of VOCs. The filtering step extracts the analyte signature from collected interferograms, while the pattern recognition procedure uses the filtered interferogram data as unique patterns to determine the presence or absence of targeted VOCs.…”

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confidence: 99%

Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements

et al. 1997

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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.

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Rapid signal processing techniques for Fourier transform infrared remote sensing

Cited by 31 publications

References 11 publications

Remote-sensing and in-situ atmospheric chemistry studies with the use of a manned hot air balloon platform

Remote-sensing and in-situ atmospheric chemistry studies with the use of a manned hot air balloon platform

Artificial Neural Networks for the Automated Detection of Trichloroethylene by Passive Fourier Transform Infrared Spectrometry

Automated Detection of Trichloroethylene by Fourier Transform Infrared Remote Sensing Measurements

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