Application of texture feature classification methods to landmine / clutter discrimination in off-lane GPR data
Recent advances in ground penetrating radar (GPR) fabrication and algorithm development have yielded significant performance improvements for anti-tank landmine detection in government sponsored blind tests. However these blind tests are typically conducted over well maintained homogeneous testing lanes specifically designed to test landmine detection performance in low-clutter population situations. New GPR data collections over targets emplaced in un-maintained off-lane soils have much higher GPR anomaly populations and provide more stringent tests of landmine detection algorithms. In this work we focus on the application of feature-based class separation techniques to lower false alarm rates in heterogeneous off-road soils. In particular we explore the application of texture feature coding methods (TFCM) which have previously shown promise in fields like tumor detection.