Automatic target recognition using neural networks

Lin-Chen Wang; Sandor Z. Der; Nasser M. Nasrabadi; Syed A. Rizvi

doi:10.1117/12.326795

9 October 1998 Automatic target recognition using neural networks

Lin-Chen Wang, Sandor Z. Der, Nasser M. Nasrabadi, Syed A. Rizvi

Proceedings Volume 3466, Algorithms, Devices, and Systems for Optical Information Processing II; (1998) https://doi.org/10.1117/12.326795
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

Composite classifiers that are constructed by combining a number of component classifiers have been designed and evaluated on the problem of automatic target recognition (ATR) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers. A number of classifier fusion algorithms are analyzed. These algorithms combine the outputs of all the component classifiers and classifier selection algorithms, which use a cascade architecture that relies on a subset of the component classifiers. Each composite classifier is implemented and tested on a large data set of real FLIR images. The performances of the proposed composite classifiers are compared based on their classification ability and computational complexity. It is demonstrated that the composite classifier based on a cascade architecture greatly reduces computational complexity with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.

Citation Download Citation

Lin-Chen Wang, Sandor Z. Der, Nasser M. Nasrabadi, and Syed A. Rizvi "Automatic target recognition using neural networks", Proc. SPIE 3466, Algorithms, Devices, and Systems for Optical Information Processing II, (9 October 1998); https://doi.org/10.1117/12.326795

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