Multiple Kernel Learning for Explosive Hazard Detection in Forward-Looking Ground-Penetrating Radar
Abstract
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 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2012
- Accession Number
- ADA582039
Entities
People
- David C. Wong
- Derek T. Anderson
- James M. Keller
- K. C. Ho
- Kevin H Stone
- Mehrdad Soumekh
- Timothy C Havens
- Tuan T. Ton
Organizations
- University of Missouri