Applications of machine-learning on detection of trace explosives using multispectral imaging

Abstract

This paper presents the algorithms to detect trace chemicals using a multi-wavelength camera. Multispectral images of the chemical and the background were collected using the Ocean Thin Films SpectroCam. The camera has an integrated motor with 8 filter color wheels and 8 interchangeable custom band pass filters in the spectral range of 200–900 nm. Since chemicals have their unique spectral reflectance, the stack of 8-dimensional image data was obtained and subsequently analyzed to develop algorithms that can uniquely identify the area where a chemical is present. In this study, we primarily used RDX, 1,3,5-Trinitroperhydro-1,3,5-triazine, the explosive component in C4. The aim of this study was to investigate the potential of the multispectral imaging system and the accuracy of the model in determining C4 chemical.