Substantial interest resides in identifying sensors, algorithms and fusion theories to detect buried explosive hazards. This is a significant research effort because it impacts the safety and lives of civilians and soldiers alike. Herein, we explore the fusion of different algorithms within and across ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors on a U.S. Army NVESD furnished experimental handheld demonstrator (EHHD) platform. Fusion is not trivial as these sensors have different sampling rates, resolutions, they observe different spatial areas and they span different portions of the electromagnetic spectrum. Herein, we investigate and compare the use of different approaches for co-registration and decision-level fusion using the Choquet integral (ChI). With respect to the ChI, we explore the impact of using a "global" (single) aggregation strategy (operator) versus tailoring different ChIs to subsets of algorithms and sensors. Receiver operating characteristic (ROC) curve results are shown for data from a U.S. Army test site containing multiple target and clutter types, burial depths and times of day.
Ryan E. Smith, Derek T. Anderson, John E. Ball, Alina Zare, and Brendan Alvey, "Aggregation of Choquet integrals in GPR and EMI for handheld platform-based explosive hazard detection," Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 1018217 (Presented at SPIE Defense + Security: April 11, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263005.
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