Histogram of gradient features for buried threat detection in ground penetrating radar data

Detection of buried explosive threats is a challenging problem. GPR has recently become a powerful tool for achieving robust subsurface target detection, but novel target types, and large numbers of subsurface objects in rural environments significantly complicate accurate discrimination of explosive threats from harmless false alarms. Significant research in feature extraction from GPR data has previously shown the capability for improved performance. Similarly, many techniques from the computer vision literature have made significant strides in recent years in for improvements in object class recognition. This work studies the relationships between and application of feature descriptor techniques from the computer vision community in application to target detection in GPR data. Relationships between a very successful computer vision technique (Histogram of Oriented Gradients) and a related powerful technique from subsurface sensing (Edge Histogram Descriptors) are explored, and preliminary results suggest that techniques from the computer vision literature may provide robust target detection performance in GPR.