Image segmentation techniques for improved processing of landmine responses in ground penetrating radar data

As ground penetrating radar sensor phenomenology improves, more advanced statistical processing approaches become applicable to the problem of landmine detection in GPR data. Most previous studies on landmine detection in GPR data have focused on the application of statistics and physics based prescreening algorithms, new feature extraction approaches, and improved feature classification techniques. In the typical framework, prescreening algorithms provide spatial location information of anomalous responses in down-track / cross-track coordinates, and feature extraction algorithms are then tasked with generating low-dimensional information-bearing feature sets from these spatial locations. However in time-domain GPR, a significant portion of the data collected at prescreener flagged locations may be unrelated to the true anomaly responses - e.g. ground bounce response, responses either temporally "before" or "after" the anomalous response, etc. The ability to segment the information-bearing region of the GPR image from the background of the image may thus provide improved performance for feature-based processing of anomaly responses. In this work we will explore the application of Markov random fields (MRFs) to the problem of anomaly/background segmentation in GPR data. Preliminary results suggest the potential for improved feature extraction and overall performance gains via application of image segmentation approaches prior to feature extraction.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering