Buried targets pose a serious threat to modern soldiers and civilians alike, thus detecting them from a safe standoff distance is an important step in their remediation. Many successful vehicle-based detection systems have been designed to utilize forward-looking ground penetrating radar (FLGPR) for buried target detection at a distance, however, FLGPR has an inherently low signal-to-clutter-ratio (SCR) so its performance is limited. To address this limitation, suites of sensors have been added to some of these vehicle-based systems. In this work we utilize data from these various sensors to improve the buried target classification accuracy. Specifically, we present features extracted from FLGPR, lidar, and thermal- and visible-spectrum camera data, then fuse the various features using a kernel-based classifier. Our results indicate that fusing these multimodal features yields a higher classification performance than utilizing data from the FLGPR alone. We also analyze each sensor's incremental improvement of classification accuracy by performing numerous experiments with different permutations of the sensors.
Anthony J. Pinar, Timothy C. Havens, and Adam Webb, "Multisensor fusion of FLGPR and thermal and visible-spectrum cameras for standoff detection of buried
objects," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101821A (Presented at SPIE Defense + Security: April 12, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2262674.
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