Emergence and distinction of classes in XRD data via machine learning

Camen Royse; Scott Wolter; Joel A. Greenberg

doi:10.1117/12.2519500

14 May 2019 Emergence and distinction of classes in XRD data via machine learning

Camen Royse, Scott Wolter, Joel A. Greenberg

Proceedings Volume 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV; 109990D (2019) https://doi.org/10.1117/12.2519500
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

The material-specific information contained in X-ray diffraction (XRD) measurements make it attractive for the detection of threats in airport baggage. Spatially-localized XRD signatures at each voxel in a bag may be obtained with a snapshot via coded aperture XRD tomography, but measurement unceratinty due to data processing and low SNR can lead to loss in information. We use machine learning and non-linear dimension reduction to identify threat and non-threat items in a way that overcomes these variations in the data. We observe the emergence of clusters from the data, possibly providing new prospects for XRD-based classification. We further show improved performance using machine learning methods relative to a conventional, correlation-based classifier in the low-SNR regime.

Citation Download Citation

Camen Royse, Scott Wolter, and Joel A. Greenberg "Emergence and distinction of classes in XRD data via machine learning", Proc. SPIE 10999, Anomaly Detection and Imaging with X-Rays (ADIX) IV, 109990D (14 May 2019); https://doi.org/10.1117/12.2519500

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