Stand-off Hyperspectral Raman Imaging and Random Decision Forest Classification: A Potent Duo for the Fast, Remote Identification of Explosives

Abstract

In this study, we present a stand-off hyperspectral Raman imager (HSRI) for the fast detection and classification of different explosives at a distance of 15 m. The hyperspectral image cube is created by using a liquid crystal tunable filter (LCTF) to select a specific Raman shift and sequentially imaging spectral images onto an intensified CCD camera. The laser beam is expanded to illuminate the field of view of the HSRI and thereby improves large area scanning of suspicious surfaces. The collected hyperspectral image cube (HSI) is evaluated and classified using a random decision forest (RDF) algorithm. The RDF is trained with a training set of mg-amounts of different explosives, i.e., TNT, RDX, PETN, NaClO₃, and NH₄NO₃, on an artificial aluminum substrate. The resulting classification is validated, and variable importance is used to optimize the RDF using spectral descriptors, effectively reducing the dimensionality of the data set. Using the gained information, a faster acquisition and calculation mode can be designed, giving improved results in classification at a much higher repetition rate.