Combination of physics-based and image-based features for landmine identification in ground penetrating radar data

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 algorithm has a different perspective to solve the 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, we propose a combined feature utilizing both physics-based and image-based techniques. Combined features are classified with a support vector machine classifier. The proposed algorithm is tested on a simulated data set that contained more than 500 innocuous object signatures and 400 landmine signatures, over half of which are completely nonmetal. The results presented indicate that the proposed method has significant performance benefits for landmine detection and identification in GPR data