Accession Number : ADA582039


Title :   Multiple Kernel Learning for Explosive Hazard Detection in Forward-Looking Ground-Penetrating Radar


Descriptive Note : Conference paper


Corporate Author : MISSOURI UNIV-COLUMBIA OFFICE OF SPONSORED PROGRAMS ADMINISTRATION


Personal Author(s) : Havens, Timothy C ; Stone, Kevin ; Anderson, Derek T ; Keller, James M ; Ho, K C ; Ton, Tuan T ; Wong, David C ; Soumekh, Mehrdad


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a582039.pdf


Report Date : Apr 2012


Pagination or Media Count : 19


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.


Descriptors :   *GROUND PENETRATING RADAR , BURIED OBJECTS , DATA FUSION , EXPLOSIVES DETECTION , FALSE ALARMS , FORWARD LOOKING


Subject Categories : Active & Passive Radar Detection & Equipment


Distribution Statement : APPROVED FOR PUBLIC RELEASE