Spectral Analysis for the Detection of Explosives with Differential Reflectometry

For explosive detection purposes, it is assumed that the person preparing or carrying the explosive will inadvertently contaminate him/herself or the exterior of the package. To detect such traces of explosive materials, we show the use of differential reflectometry (DR) as an alternative system to the existing techniques. With DR, explosives show characteristic behaviours at specific wavelengths, for example, spectra of TNT shows a sudden decrease at 420 nm. To detect these behaviours, principle component analysis was performed to reduce the dimensionality of the data, and a support vector machine classifier was trained to identify TNT. With a 10-fold classification on 10000 non-TNT and 1935 TNT pixels, we achieved 0.3% false alarm rate at 75% true positive rate. In this study, we outline the operation of the DR system, show the unique signatures of explosives when viewed with DR, and report the detection rates with support vector machine classifiers.