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A GPR-based landmine identification method using energy and dielectric features

Akar, Gözde
GENÇ, alper
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 time length of the reflected GPR signal from the underground object indicates the first feature. The second feature estimates the intrinsic impedance of the buried object. These two features are classified with the 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.