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1 April 1997 Automatic target recognition using a modular neural network with directional variance features
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A modular neural network classifier has been applied to the problem of automatic target recognition (ATR) using forward- looking infrared (FLIR) imagery. This modular network classifier consists of several neural networks (expert networks) for classification. Each expert network in the modular network classifier receives distinct inputs from features extracted from only a local region of a target, known as a receptive field, and is trained independently from other expert networks. The classification decisions of the individual expert networks are combined to determine the final classification. Our experiments show that this modular network classifier is superior to a fully connected neural network classifier in terms of complexity (number of weights to be learned) and performance (probability of correct classification). The proposed classifier shows a high noise immunity to clutter or target obscuration due to the independence of the individual neural networks in the modular network, 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 expert networks, a method known as stacked generalization.
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Lin-Cheng Wang, Sandor Z. Der, Syed A. Rizvi, and Nasser M. Nasrabadi "Automatic target recognition using a modular neural network with directional variance features", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997);

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