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Proceedings Paper

A Pareto evolutionary artificial neural network approach for remote sensing image classification
Author(s): Fujiang Liu; Xincai Wu; Yan Guo; Huashan Sun; Feng Zhou; Linlu Mei
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Paper Abstract

This paper presents a Pareto evolutionary artificial neural network (Pareto-EANN) approach based on the evolutionary algorithms for multiobjective optimization augmented with local search for the classification of remote sensing image. Its novelty lies in the use of a multiobjective genetic algorithm where single hidden layers Multilayer Perceptrons (MLP) are employed to indicate the accuracy/complexity trade-off. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy of the classifier and number of hidden units. We compared Pareto-EANN classifiers results of the classification of remote sensing image against standard backpropagation neural network classifiers and EANN classifiers; we show experimentally the efficiency of the proposed methodology.

Paper Details

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191L (28 October 2006); doi: 10.1117/12.713258
Show Author Affiliations
Fujiang Liu, China Univ. of Geosciences (China)
Xincai Wu, China Univ. of Geosciences (China)
Yan Guo, China Univ. of Geosciences (China)
Huashan Sun, China Univ. of Geosciences (China)
Feng Zhou, China Univ. of Geosciences (China)
Linlu Mei, China Univ. of Geosciences (China)


Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

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