Robust detection and fusion of mine images

Current research in minefield detection indicates that operationally no single sensor technology will likely be capable of detecting mines/minefields in a real-time manner and at a performance level suitable for a forward maneuver unit. Minefield detection involves a particularly wide range of operating scenarios and environmental conditions, which requires deployment of complementary sensor suites. Consequently, the NVESD sponsored Signal Processing and Algorithm Development for Robust Mine Detection (SPAD) Program is currently focusing on the development of computationally efficient and robust detection algorithms applicable to a variety of sensors and on the development of a robust decision level fusion algorithm that exploits these detectors. One SPAD detection technique, called the Ellipse Detector, has been previously reported in the open literature. We briefly report on the continued robust performance of this detector on some new sensor output. We also report on another robust detector developed for sensors that produce output not suitable for the Ellipse Detector. However, the focus of this paper is on the SPAD decision level fusion algorithm, called the Piecewise Level Fusion Algorithm (PLFA). We emphasize the robustness and flexibility of the PLFA architecture by describing its performance and results for both multisensor and multilook fusion.