Dictionary Learning for Adaptive GPR Landmine Classification

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-times singular value decomposition (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 (ODL), 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 the 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.