Optimized Spectral Transformation for Detection and Classification of Buried Radioactive Materials

We investigate the detection and classification of buried radioactive materials of interest using data collected by a sodium iodide (NaI) detector with a short sensor dwell time (i.e., less than or equal to 1 s). The objective of detection is to detect a target from background and nontarget materials, while the objective of classification is to classify targets buried at different depths. Binned energy windows can reduce data dimensionality, help alleviate the negative impact from background, and suppress trivial spectral variations. However, the performance is sensitive to bin partition parameters including the number of bins and their bin widths. We have developed a particle swarm optimization (PSO)-based automatic system to determine these parameters. We also propose to apply a multiobjective PSO to optimize both the detection and classification accuracy simultaneously. The experimental results show that the PSO-based algorithm can outperform the Powell's direction set optimization method. The multiobjective PSO can achieve the balance between the two objectives, and it may provide even better individual accuracy than a single-objective PSO.