Neural-network-based target detection system for FLIR imagery

Chris M. Dwan; Sandor Z. Der

doi:10.1117/12.304655

1 April 1998 Neural-network-based target detection system for FLIR imagery

Chris M. Dwan, Sandor Z. Der

Proceedings Volume 3307, Applications of Artificial Neural Networks in Image Processing III; (1998) https://doi.org/10.1117/12.304655
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

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 (P_d). 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.

Citation Download Citation

Chris M. Dwan and Sandor Z. Der "Neural-network-based target detection system for FLIR imagery", Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); https://doi.org/10.1117/12.304655

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