Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans

Abstract

Under study is an application of Ground Penetrating Radar (GPR) to landmine detection problem. We focus on the detection of antitank mines carried out in the 3D GPR images, so-called C-scans, by means of a machine learning approach. In that approach, we particularly pursue a technique for fast extraction of image features based on an initial calculation of multiple integral images. This allows later to calculate each feature in constant time, regardless of the scanning window position and size. The features we study are statistical moments formulated in their 3D variant. We present a comparison of detection results for different sizes and parameterizations of feature sets. All results are obtained from a prototype GPR system of our original construction in terms of both hardware and software.