Emergence and distinction of classes in XRD data via machine learning

Camen Royse; Scott Wolter; Joel A. Greenberg

doi:10.1117/12.2519500

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

Camen Royse; Scott Wolter; Joel A. Greenberg

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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, Maryland, 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.

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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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