Ground penetrating radar (GPR) systems have emerged as a state-of-the-art remote sensing platform for the automatic detection of buried explosive threats. The GPR system that was used to collect the data considered in this work consists of an array of radar antennas mounted on the front of a vehicle. The GPR data is collected as the vehicle moves forward down a road, lane or path. The data is then processed by computerized algorithms that are designed to automatically detect the presence of buried threats. The amount of GPR data collected is typically prohibitive for real-time buried threat detection and therefore it is common practice to first apply a prescreening algorithm in order to identify a small subset of data that will then be processed by more computationally advanced algorithms. Historically, the F1V4 anomaly detector, which is energy-based, has been used as the prescreener for the GPR system considered in this work. Because F1V4 is energy-based, it largely discards shape information, however shape information has been established as an important cue for the presence of a buried threat. One recently developed prescreener, termed the HOG prescreener, employs a Histogram of Oriented Gradients (HOG) descriptor to leverage both energy and shape information for prescreening. To date, the HOG prescreener yielded inferior performance compared to F1V4, even though it leveraged the addition of shape information. In this work we propose several modifications to the original HOG prescreener and use a large collection of GPR data to demonstrate its superior detection performance compared to the original HOG prescreener, as well as to the F1V4 prescreener.
Daniël Reichman, Leslie M. Collins, and Jordan M. Malof, "Improvements to the Histogram of Oriented Gradient (HOG) prescreener for buried threat detection in ground penetrating radar data," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820U (Presented at SPIE Defense + Security: April 11, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263107.
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