Comparison of pattern recognition approaches for multi-sensor detection and discrimination of anti-personnel and anti-tank landmines

In this work we explore and compare several statistical pattern recognition techniques for classification and identification of buried landmines using both electromagnetic induction and ground penetrating radar data. In particular we explore application of different feature extraction approaches to the problem of landmine/clutter classification in blind- and known- ground truth scenarios using data from the NIITEK ground penetrating radar and the Vallon EMI sensor as well as the CyTerra GPR and Minelab EMI sensors. We also compare and contrast the generalization capabilities of different kernels including radial basis function, linear, and direct kernels within the relevance vector machine framework. Results are presented for blind-test scenarios that illustrate robust classification for features that can be extracted with low computational complexity.