  Ground penetrating radar (GPR) target detection and classification is a
challenging task. Here, we consider online dictionary learning (DL) methods to
obtain sparse representations (SR) of the GPR data to enhance feature
extraction for target classification via support vector machines. Online
methods are preferred because traditional batch DL like K-SVD is not scalable
to high-dimensional training sets and infeasible for real-time operation. We
also develop Drop-Off MINi-batch Online Dictionary Learning (DOMINODL) which
exploits the fact that a lot of the training data may be correlated. The
DOMINODL algorithm iteratively considers elements of the training set in small
batches and drops off samples which become less relevant. For the case of
abandoned anti-personnel landmines classification, we compare the performance
of K-SVD with three online algorithms: classical Online Dictionary Learning,
its correlation-based variant, and DOMINODL. Our experiments with real data
from L-band GPR show that online DL methods reduce learning time by 36-93% and
increase mine detection by 4-28% over K-SVD. Our DOMINODL is the fastest and
retains similar classification performance as the other two online DL
approaches. We use a Kolmogorov-Smirnoff test distance and the
Dvoretzky-Kiefer-Wolfowitz inequality for the selection of DL input parameters
leading to enhanced classification results. To further compare with
state-of-the-art classification approaches, we evaluate a convolutional neural
network (CNN) classifier which performs worse than the proposed approach.
Moreover, when the acquired samples are randomly reduced by 25%, 50% and 75%,
sparse decomposition based classification with DL remains robust while the CNN
accuracy is drastically compromised.
