An Application of CNNs to Time Sequenced One Dimensional Data in Radiation Detection
| Autor: | Moore, Eric T., Ford, William P., Hague, Emma J., Turk, Johanna |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861C (14 May 2019); https://doi.org/10.1117/12.2519037 |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 11 pages, 9 figures, presented: SPIE Defense + Commercial Sensing, 16-18 Apr 2019, Baltimore, MD, United States |
| Databáze: | arXiv |
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