Automatic Identification of Training & Testing Data for Buried Threat Detection using Ground Penetrating Radar

Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. The radar is attached to front of a vehicle and collects measurements on the path of travel. At each spatial location queried, a time-series of measurements is collected, and then the measured set of data are often visualized as images within which the signals corresponding to buried threats exhibit a characteristic appearance. This appearance is typically hyperbolic and has been leveraged to develop several automated detection methods. Many of the detection methods applied to this task are supervised, and therefore require labeled examples of threat and non-threat data for training. Labeled examples are typically obtained by collecting data over deliberately buried threats at known spatial locations. However, uncertainty exists with regards to the temporal locations in depth at which the buried threat signal exists in the imagery. This uncertainty is an impediment to obtaining labeled examples of buried threats to provide to the supervised learning model. The focus of this dissertation is on overcoming the problem of identifying training data for supervised learning models for GPR buried threat detection.

The ultimate goal is to be able to apply the lessons learned in order to improve the performance of buried threat detectors. Therefore, a particular focus of this dissertation is to understand the implications of particular data selection strategies, and to develop principled general strategies for selecting the best approaches. This is done by identifying three factors that are typically considered in the literature with regards to this problem. Experiments are conducted to understand the impact of these factors on detection performance. The outcome of these experiments provided several insights about the data that can help guide the future development of automated buried threat detectors.

The first set of experiments suggest that a substantial number of threat signatures are neither hyperbolic nor regular in their appearance. These insights motivated the development of a novel buried threat detector that improves over the state-of-the-art benchmark algorithms on a large collection of data. In addition, this newly developed algorithm exhibits improved characteristics of robustness over those algorithms. The second set of experiments suggest that automating the selection of data corresponding to the buried threats is possible and can be used to replace manually designed methods for this task.