Boosted Classifiers for Antitank Mine Detection in C-Scans from Ground-Penetrating Radar

Abstract

We investigate the problem of automatic antitank mine detection. Subject to detection are 3D images, so-called C-scans, generated by a GPR (Ground- Penetrating Radar) system of our construction. In the paper we focus on boosting as a machine learning approach well suited for large-scale data such as GPR data. We compare five variants of weak learners with real-valued responses trained by the same boosting scheme. Three of the variants are single-feature-based learners that differ in theway they approximate class conditional distributions. The two remaining variants are shallow decision trees, respectively, with four and eight terminal nodes, introducing joint-feature conditionals.