A Partially Supervised Approach for Detection and Classification of Buried Radioactive Metal Targets Using Electromagnetic Induction Data

The analysis of the data obtained from electromagnetic induction (EMI) sensors is one of the most viable tools for the detection of metallic objects buried under soil. The existing detection methods usually consist of sophisticated EM modeling of the source/target geometry to build suitable discriminators. The major technical challenge in this field is the reduction of false alarms with an increase of the detection probability. In this paper, we propose a partially supervised approach to detect buried radioactive targets, i.e., depleted uranium, without sophisticated EM modeling. Using the EMI data obtained by a GEM-3 sensor for a field survey, our proposed algorithm can successfully detect and discriminate the targets from nontarget metals, compared to other unsupervised and supervised approaches.