On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection

Abstract

Ground-penetrating radar (GPR) is one of the most popular and successful sensing modalities that have been investigated for landmine and subsurface threat detection. Many of the detection algorithms applied to this task are supervised and therefore require labeled examples of threat and nonthreat data for training. Training data most often consist of 2-D images (or patches) of GPR data, from which features are extracted and provided to the classifier during training and testing. Identifying desirable training and testing locations to extract patches, which we term “keypoints,” is well established in the literature. In contrast, however, a large variety of strategies have been proposed regarding keypoint utilization (e.g., how many of the identified keypoints should be used at threat, or nonthreat, locations). Given a variety of keypoint utilization strategies that are available, it is very unclear: 1) which strategies are best or 2) whether the choice of strategy has a large impact on classifier performance. We address these questions by presenting a taxonomy of existing utilization strategies and then evaluating their effectiveness on a large data set using many different classifiers and features. We analyze the results and propose a new strategy, called PatchSelect, which outperforms other strategies across all experiments.