A committee of networks classifier with multi-resolution feature extraction for automatic target recognition

Abstract

A neural network-based classifier has been applied to the problem of automatic target recognition (ATR) using forward-looking infrared (FLIR) imagery. The target classifier consists of several neural networks that form a committee for classification. Each neural network in the committee receives inputs from features extracted from only a local region of a target, known as a receptive field, and is trained independently from other committee members. The classification results of the individual neural networks are combined to determine the final classification. Our experiments show that this committee of networks classifier is superior to a fully connected neural network classifier in terms of complexity (number of weights to be learned) and performance (classification rate). The proposed classifier shows a high noise immunity to clutter or target obscuration due to the independence of the individual neural networks in the committee. Performance of the proposed classifier is further improved by the use of multi-resolution features and by the introduction of a higher level neural network on the top of committee, a method known as stacked generalization.