Physics-based features for identifying contextual factors affecting landmine detection with ground-penetrating radar

It has been established throughout the ground-penetrating radar (GPR) literature that environmental factors can severely impact the performance of GPR sensors in landmine detection applications. Over the years, electromagnetic inversion techniques have been proposed for determining these factors with the goal of mitigating performance losses. However, these techniques are often computationally expensive and require models and responses from canonical targets, and therefore may not be appropriate for real-time route-clearance applications. An alternative technique for mitigating performance changes due to environmental factors is context-dependent classification, in which decision rules are adjusted based on contextual shifts identified from the GPR data. However, analysis of the performance of context-dependent learning has been limited to qualitative comparisons of contextually-similar GPR signatures and quantitative improvement to the ROC curve, while the actual information extracted regarding soils has not been investigated thoroughly. In this work, physics-based features of GPR data used in previous context-dependent approaches were extracted from simulated GPR data generated through Finite-Difference Time-Domain (FDTD) modeling. Statistical techniques where then used to predict several potential contextual factors, including soil dielectric constant, surface roughness, amount of subsurface clutter, and the existence of subsurface layering, based on the features. Results suggest that physics-based features of the GPR background may contain informatin regarding physical properties of the environment, and contextdependent classification based on these features can exploit information regarding these potentially-important environmental factors. © 2011 SPIE.