Committee classifier based on linear discriminant analysis for the detection of radioisotopes from airborne gamma-ray spectra

Abstract

A committee classifier was developed for use in the application of real-time pattern recognition to gamma-ray spectra collected from airborne surveys. This technique was designed to enhance detection performance relative to that of a single linear discriminant analysis model. The approach was based on utilizing multiple classifiers to check one another through a signal averaging method. This resulted in an ability to reject random false detections while maximizing detection sensitivity. Making use of spectral preprocessing algorithms previously studied, the committee classifiers were applied to the detection of cesium-137 and cobalt-60 in spectra collected in the field during airborne surveys. Applying a z-score methodology to the classification scores allowed classifiers developed with different processing parameters to operate in the same dataspace for the purpose of classifying the target spectra. The optimized classifiers were tested over 13 diverse locations, with nine of the sites containing the respective target isotopes. Results of the committee classifiers indicated an improvement in missed and false detection performance for both radioisotopes. In addition, work was performed to confirm that several suspected false detections were actually weak target signals only visible once co-added with other similar spectra. This result suggested the committee classifier performance may have exceeded the capabilities of the visual spectral inspection on which the performance statistics were based.