Application of texture feature classification methods to landmine / clutter discrimination in off-lane GPR data

Published

Conference Paper

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.

Duke Authors

Cited Authors

  • Torrione, P; Collins, L

Published Date

  • December 1, 2004

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Volume / Issue

  • 3 /

Start / End Page

  • 1621 - 1624

Citation Source

  • Scopus