Feature discovery via neural networks for object recognition in SAR imagery

A two-stage self-organizing neural network architecture has been applied to object recognition in synthetic aperture radar imagery. The first stage performs feature extraction and implements a two-layer neocognitron. The resulting feature vectors are presented to the second stage, an ART 2-A classifier network, which clusters the features into multiple target categories. Training is performed off-line in two steps. First, the neocognitron self-organizes in response to repeated presentations of an object to recognize. During this training process, discovered features and the mechanisms for their extraction are captured in the excitatory weight patterns. In the second step, neocognitron learning is inhibited and the ART 2-A classifier forms categories in response to the feature vectors generated by additional presentations of the object to recognize. Finally, all training is inhibited and the system tested against a variety of objects and background clutter. The results of the initial experiments are reported