期刊
IMAGE AND VISION COMPUTING
卷 19, 期 9-10, 页码 699-707出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/S0262-8856(01)00045-2
关键词
neural network ensembles; combination of classifiers; image classification; remote sensing
In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of 'error-independent' networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles. (C) 2001 Elsevier Science B.V. All rights reserved.
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