Signature Evaluation for Thermal Infrared Countermine and IED Detection Systems

A high-resolution, computational suite has been constructed to produce synthetic thermal imagery of vegetated soil surfaces with landmines or other targets. The imagery is created by coupling models for the ground, vegetation, ray casting, and sensor characteristics to produce realistic thermal infrared simulated imagery. These simulations provide information ranging from simple temperature contrasts to high-resolution images comparable to actual sensor images that can be used to evaluate or train automatic target recognition (ATR) systems. Analyses of the ATR results allow development of recommendations for optimal sensing strategies and additional training to improve ATR performance. The modeling and characterization occurs at the centimeter scale, which requires massively parallel computational resources to meet the demands of the simulation. The models run simultaneously on a single, parallel, or serial computer and communicate using sockets or files. The soil model is a three-dimensional, spatially adaptive, continuous Galerkin, finite element model that simulates partially-saturated flow and heat transport, coupled to two-dimensional surface water flow. The vegetation model simulates infrared absorption, reflection, and transmission by discretized plant leaves and stems. Ray casting provides boundary conditions for the soil and vegetation thermal models, and produces multi-spectral images of energy reflected and emitted from the synthetic scene. Subsurface phase change, distributed root zone moisture uptake and transpiration, and flow through macro pores and cracks are processes under construction. The parallelization of the individual testbed components is relatively straight-forward. The central difficulty in achieving acceptable performance for the computational testbed in a parallel computing environment is the sequencing of data transfers between components. Example calculations to be presented include a multi-million element simulation for an arid test site that is only a few meters in its longest dimension. The models are driven with meteorological data and are built using material property data collected at the field site. Synthetic images produced are compared against those from thermal cameras. A long-term goal of this work is to help build parameter estimation software to infer ground state information (soil moisture and physical property distributions) from airborne imagery.