Multiple kernel learning for explosive hazard detection in forward-looking ground-penetrating radar

This paper proposes an effective anomaly detection algorithm for forward-looking ground-penetrating radar (FLGPR). The challenges in detecting explosive hazards with FLGPR are that there are multiple types of targets buried at different depths in a highly-cluttered environment. A wide array of target and clutter signatures exist, which makes classifier design difficult. Recent work in this application has focused on fusing the classifier results from multiple frequency subband images. Each sub-band classifier is trained on suites of image features, such as histogram of oriented gradients (HOG) and local binary patterns (LBP). This prior work fused the sub-band classifiers by, first, choosing the top-ranked feature at each frequency sub-band in the training data and then accumulating the sub-band results in a confidence map. We extend this idea by employing multiple kernel learning (MKL) for feature-level fusion. MKL fuses multiple sources of information and/or kernels by learning the weights of a convex combination of kernel matrices. With this method, we are able to utilize an entire suite of features for anomaly detection, not just the top-ranked feature. Using FLGPR data collected at a US Army test site, we show that classifiers trained using MKL show better explosive hazard detection capabilities than single-kernel methods.