High-pass filters for spectral background suppression in airborne passive Fourier transform infrared spectrometry

Abstract

High-pass (HP) digital filtering and second-derivative (SD) filtering are evaluated as methods of removing background contributions from spectra collected by passive Fourier transform infrared spectrometry. In measurements performed with a downward-looking spectrometer mounted on an aircraft platform, the effects of non-constant background radiance from the ground make it challenging to build automated classifiers for detecting an analyte of interest. Applying HP digital filtering to the spectra to remove background contributions is evaluated as a strategy to help improve classifier performance. This methodology is tested by building classifiers for detecting heated ethanol plumes released from a portable emission stack. The classifiers are trained with data collected on the ground with the spectrometer viewing the plumes against a synthetic backdrop designed to simulate a terrestrial radiance source. The resulting classifiers are tested with data collected by the same spectrometer mounted on an aircraft flying over the emission stack. Support vector machines are employed as a classification algorithm with HP filtered spectra used as input patterns. Butterworth filters are used to implement HP digital filtering, while Savitzky–Golay filters are used to implement SD filtering. Significant improvement in classification performance is achieved by use of the HP filters. Because of variation in backgrounds between the training and prediction data, the best classifier obtained with unfiltered spectra is unable to detect ethanol in 37% of the test cases. HP filtering of spectra with an optimized Butterworth filter (order 8, cutoff frequency 0.060) improves the prediction results, resulting in no missed ethanol detections and false positive rates of less than 0.4%.