Multi-feature fusion for GPR-based landmine detection and classification

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2019
Genç, Alper
Ground penetrating radar (GPR) is a powerful technology for detection and identification of buried explosives especially with little or no metal content. However, subsurface clutter and soil distortions increase false alarm rates of current GPR-based landmine detection and identification methods. Most existing algorithms use shape- based, image-based and physics-based techniques. Analysis of these techniques indicates that each type of algorithms has a different perspective to solve landmine detection and identification problem. Therefore, one type of method has stronger and weaker points with respect to the other types of algorithms. To reduce false alarm rates of the current GPR-based landmine detection and identification methods, this study proposes a combined feature utilizing both physics- based and image-based techniques. Combined features are classified with support vector machine (SVM) classifier. The proposed algorithm is tested on a simulated data set contained more than 400 innocuous object signatures and 300 landmine signatures, over half of which are completely nonmetal. The results presented indicate that the proposed method in this study has significant performance benefits for landmine detection and identification in GPR data even in cluttered environment.
Citation Formats
A. Genç, “Multi-feature fusion for GPR-based landmine detection and classification,” Ph.D. - Doctoral Program, Middle East Technical University, 2019.