An application of CNNs to time sequenced one dimensional data in radiation detection

Eric T. Moore; William P. Ford; Emma J. Hague; Johanna Turk

doi:10.1117/12.2519037

14 May 2019 An application of CNNs to time sequenced one dimensional data in radiation detection

Eric T. Moore, William P. Ford, Emma J. Hague, Johanna Turk

Proceedings Volume 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV; 109861C (2019) https://doi.org/10.1117/12.2519037
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

A Convolutional Neural Network architecture was used to classify various isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation detection system of a type commonly fielded for security or environmental measurement purposes. A two-dimensional surface (waterfall plot) in time-energy space is interpreted as a monochromatic image and standard image-based CNN techniques are applied. This allows for the time-sequenced aspects of features in the data to be discovered by the network, as opposed to standard algorithms which arbitrarily time bin the data to satisfy the intuition of a human spectroscopist. The CNN architecture and results are presented along with a comparison to conventional techniques. The results of this novel application of image processing techniques to radiation data will be presented along with a comparison to more conventional adaptive methods.¹

Conference Presentation

Citation Download Citation

Eric T. Moore, William P. Ford, Emma J. Hague, and Johanna Turk "An application of CNNs to time sequenced one dimensional data in radiation detection", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861C (14 May 2019); https://doi.org/10.1117/12.2519037

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