A hybrid neural network architecture for automatic object recognition

This paper describes the application of a hybrid neural network architecture for automatic object recognition in inverse synthetic aperture radar (ISAR) imagery. The architecture employs a cascaded combination of an unsupervised and a supervised trained neural network. The unsupervised trained self-organizing feature map is used for object segmentation and the supervised trained multilayer perceptron classifies the segmented objects. The classification result is fed back to the feature map segmentor in order to improve segmentation and classification. The functionality of this approach is demonstrated by the use of simulated noisy ISAR images from different objects.<>