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Proceedings Paper

Threat determination for radiation detection from the Remote Sensing Laboratory
Author(s): William P. Ford; Emma Hague; Tom McCullough; Eric Moore; Johanna Turk
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Paper Abstract

The ability to search for radiation sources is of interest to the Homeland Security community. The hope is to find any radiation sources which may pose a reasonable chance for harm in a terrorist act. The best chance of success for search operations generally comes with fielding as many detection systems as possible. In doing this, the hoped for encounter with the threat source will inevitably be buried in an even larger number of encounters with non-threatening radiation sources commonly used for many medical and industrial use. The problem then becomes effectively filtering the non-threatening sources, and presenting the human-in-the-loop with a modest list of potential threats. Our approach is to field a collection of detection systems which utilize soft-sensing algorithms for the purpose of discriminating potential threat and non-threat objects, based on a variety of machine learning techniques.

Paper Details

Date Published: 8 May 2018
PDF: 7 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440G (8 May 2018); doi: 10.1117/12.2305047
Show Author Affiliations
William P. Ford, Remote Sensing Lab. (United States)
Emma Hague, Remote Sensing Lab. (United States)
Tom McCullough, Remote Sensing Lab. (United States)
Eric Moore, Remote Sensing Lab. (United States)
Johanna Turk, Remote Sensing Lab. (United States)

Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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