Multiple Kernel Learning for Explosive Hazard Detection in Forward-Looking Ground-Penetrating Radar
MISSOURI UNIV-COLUMBIA OFFICE OF SPONSORED PROGRAMS ADMINISTRATION
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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 sub-band 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 andor 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.
- Active and Passive Radar Detection and Equipment