Modeling Clutter and Context for Target Detection in Infrared Images

In order t o reduce false alarms and t o improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important t o develop the models not only for man-made targets but also of natural background clutters. Because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. If available, contextual information that characterizes each training example can be used to further improve the learned clutter model. In this paper, we present such a clutter model a ided target detection system. Emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.