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An application of CNNs to time sequenced one dimensional data in radiation detection
Author(s): Eric T. Moore; William P. Ford; Emma J. Hague; Johanna Turk
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Paper 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

Paper Details

Date Published: 14 May 2019
PDF: 11 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861C (14 May 2019); doi: 10.1117/12.2519037
Show Author Affiliations
Eric T. Moore, Remote Sensing Lab. (United States)
William P. Ford, Remote Sensing Lab. (United States)
Emma J. Hague, Remote Sensing Lab. (United States)
Johanna Turk, Barnstorm Research Corp. (United States)


Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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