Generative adversarial networks for ground penetrating radar in hand held explosive hazard detection

The identification followed by avoidance or removal of explosive hazards in past and/or present conflict zones is a serious threat for both civilian and military personnel. This is a challenging task as extreme variability exists with respect to the objects, their environment and emplacement context. A goal is the development of automatic, or human-in-the-loop, sensor technologies that leverage engineering theories like signal processing, data fusion and machine learning. Herein, we explore the detection of buried explosive hazards (BEHs) in handheld ground penetrating radar (HH-GPR) via convolutional neural networks (CNNs). In particular, we investigate the potential for generative adversarial networks (GANs) to impute new data based on limited and class imbalance labeled data. Unsupervised GANs are trained and assessed at a qualitative level and their outputs are explored in different ways to quantitatively help train a CNN classifier. Overall, we found encouraging qualitative results and a list of hurdles that need to be overcome before we anticipate quantitative improvements.