This study presents a novel landmine identification method that estimates intrinsic parameters of buried objects from their primary and secondary GPR reflections to reduce false alarm rates of GPR-based landmine detection algorithms. To achieve this, two different features are extracted from A-scan GPR data of buried objects. The first feature identifies significant GPR signal length. The second feature estimates intrinsic impedance of the object. These two features are classified with support vector machine (SVM) classifier. The experimental results show that the proposed features have very high discrimination power which reduces false alarm rates to a great extent.
Alper Genç and Gözde Bozdaği Akar, "A GPR-based landmine identification method using energy and dielectric features," Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 1062809 (Presented at SPIE Defense + Security: April 16, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2301009.
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