A Pareto evolutionary artificial neural network approach for remote sensing image classification

Fujiang Liu; Xincai Wu; Yan Guo; Huashan Sun; Feng Zhou; Linlu Mei

doi:10.1117/12.713258

28 October 2006 A Pareto evolutionary artificial neural network approach for remote sensing image classification

Fujiang Liu, Xincai Wu, Yan Guo, Huashan Sun, Feng Zhou, Linlu Mei

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64191L (2006) https://doi.org/10.1117/12.713258
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

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.

Citation Download Citation

Fujiang Liu, Xincai Wu, Yan Guo, Huashan Sun, Feng Zhou, and Linlu Mei "A Pareto evolutionary artificial neural network approach for remote sensing image classification", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191L (28 October 2006); https://doi.org/10.1117/12.713258

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