Classification-Aware Dimensionality Reduction Methods for Explosives Detection using Multi-Energy X-ray Computed Tomography

摘要

Multi-Energy X-ray Computed Tomography (MECT) is a non-destructive scanning technology in which multiple energy-selective measurements of the X-ray attenuation can be obtained. This provides more information about the chemical composition of the scanned materials than single-energy technologies and potential for more reliable detection of explosives. We study the problem of discriminating between explosives and non-explosives using low-dimensional features extracted from the high-dimensional attenuation versus energy curves of materials. We study various linear dimensionality reduction methods and demonstrate that the detection performance can be improved by using more than two features and when using features different than the standard photoelectric and Compton coefficients. This suggests the potential for improved detection performance relative to conventional dual-energy X-ray systems.