2016/12/11 by Daniël Reichman, Reichman, Daniël, Leslie M. Collins +3
Computer Science · Earth and Planetary Sciences · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geophysical Methods and Applications #Geophysical and Geoelectrical Methods #Seismic Waves and Analysis #Seismology and Earthquake Studies #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.03477
9 pages, 8 figures, journal paper
arxiv created 2016/12/11 · openalex publication_date 2016/12/11 · arxiv updated 2016/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has 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 target and non-target data for training. Training data most often consists of 2-dimensional 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 targets, or non-target, locations). Given the variety keypoint utilization strategies that are available, it is very unclear (i) which strategies are best, or (ii) 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 dataset using many different classifiers and features. We analyze the results and propose a new strategy, called PatchSelect, which outperforms other strategies across all experiments.