Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (Ba-133, Na-22, Cs-137, and Co-60). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an insitu monitoring system. (C) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC.

Automated summary

This paper proposes a sparse representation with dictionary learning approach for identifying radioactive isotopes in plastic gamma-ray spectra. Monte Carlo simulation is used to generate learning samples, and experimental measurements are conducted to obtain practical spectra. The tested dictionaries show good accuracy for different source positions and measurement times, and acceptable performance when the spectra are artificially shifted.