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A preliminary analysis of a sparse reconstruction based classification method applied to GPR data

This work investigates the performance of a sparse decomposition based approach applied to measured Ground Penetrating Radar (GPR) datasets for landmine recognition. The decomposition of the datasets is achieved via the solution of a constraint-relaxed convex optimization problem known as Basis Pursuit Denoise (BPDN). We demonstrate that it is crucial to appropriately construct a database of known scattering responses from mines and clutter, which will form the so-called dictionary. The robustness and accuracy of the methodology are evaluated against different parameters such as the size of the dictionary, the number and selection of time samples and the regularization parameter (noise estimate). Achieved performances are then assessed using the probability of detection and false alarm rate. As figure of merit for the classification accuracy, we use an additional measure, the so called Sparsity Concentration Index (SCI). For validation purposes, we finally compare the classification performance of the presented strategy with another sparse reconstruction based technique (Orthogonal Matching Pursuit, OMP) and an algorithm based on Support Vector Machines (SVM). The obtained results evidence that the proposed method is not only able to discriminate between targets and clutter, but also to recognize the particular type of mine simulants present in the evaluated surveys.