The ground penetrating radar (GPR) is a popular remote sensing modality for buried threat detection. In this work we focus on the development of supervised machine learning algorithms that automatically identify buried threats in GPR data. An important step in many of these algorithms is feature extraction, where statistics or other measures are computed from the raw GPR data, and then provided to the machine learning algorithms for classification. It is well known that an effective feature can lead to major performance improvements and, as a result, a variety of features have been proposed in the literature. Most of these features have been handcrafted, or designed through trial and error experimentation. Dictionary learning is a class of algorithms that attempt to automatically learn effective features directly from the data (e.g., raw GPR data), with little or no supervision. Dictionary learning methods have yielded state-of-theart performance on many problems, including image recognition, and in this work we adapt them to GPR data in order to learn effective features for buried threat classification. We employ the LC-KSVD algorithm, which is a discriminative dictionary learning approach, as opposed to a purely reconstructive one like the popular K-SVD algorithm. We use a large collection of GPR data to show that LC-KSVD outperforms two other approaches: the popular Histogram of oriented gradient (HOG) with a linear classifier, and HOG with a nonlinear classifier (the Random Forest).
Jordan M. Malof, Daniël Reichman, and Leslie M. Collins, "Discriminative dictionary learning to learn effective features for detecting buried threats in ground penetrating radar data," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820W (Presented at SPIE Defense + Security: April 11, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263111.
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