  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.
