Robust abnormality detection methods for spatial search of radioactive materials

Myeong Hun Jeong, Clair J. Sullivan, Yizhao Gao, Shaowen Wang

Research output: Contribution to journalArticle

Abstract

Radiological dirty bombs and improvised nuclear devices pose a significant threat to both public health and national security. Growing networks of radiation sensors have been deployed by a number of offices within the U.S. and international agencies. Detecting such threats while minimizing false alarm rates presents a considerable challenge to homeland security and public health. This research aims to achieve a higher probability of detection with a lower probability of false alarms. It focuses on the local spatial instability of radiation levels in order to detect radioactive materials based on robust outlier detection methods. Our approach includes a three-step abnormality detection method consisting of one-dimensional robust outlier detection for all gamma-ray counts, a density-based clustering analysis, and a two-dimensional robust outlier detection method using a bagplot, based on spatial associations. The effectiveness of the method proposed is demonstrated through a case study, wherein radioactive materials are detected in urban environments, and its performance is compared with alternative methods employing a k-sigma approach, local Getis–Ord (G*j>) statistic, and the goodness of fit of the Poisson distribution.

Original languageEnglish (US)
Pages (from-to)860-877
Number of pages18
JournalTransactions in GIS
Volume23
Issue number4
DOIs
StatePublished - Jan 1 2019

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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