Abstract
This paper describes a Neural Network based target detection system for Forward-Looking Infrared (FLIR) imagery. We apply a series of four algorithms (detection, two layers of clutter rejection and one of centering) to successively reduce the False Alarm Rate while maintaining a high probability of detection (Pd). The detection stage scans the entire image to find regions approximately the size of a target with pixel statistics that differ from their local background. The clutter rejection stages eliminate portions of these detections, while the centering algorithm moves each detection to the point near it which is most like prior examples of perfectly centered targets. The system was trained and tested on a large set of second generation FLIR data.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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