A Novel Feature Extraction Algorithm for IED Detection from 2-D Images using Minimum Connected Components

Buried Improvised Explosive Devices (IEDs) have become a significant threat to security forces combating terrorism. The detection of these concealed threats is a very challenging task. Ground Penetrating Radar (GPR) has shown promise in the detection of buried metallic and non-metallic IEDs or their components. The GPR produces a 2-D image of radar returns reflected off the buried objects. The challenge is how to detect IED's in the presence of strong backscatter. In this paper, a Graph Theory based approach known as Minimum Connected Component (MCC) has been applied to detect buried objects from the 2-D images produced by the GPR. The MCC feature extraction algorithm efficiently extracted the IED component from ten different data sets collected by the GPR. The uniqueness of the algorithm is that it extracts the image of the IED without any user specified threshold or any user inputs.