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.

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

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