Evolving Neural Network Using Variable StringGenetic Algorithms (VGA) for Color Infrared Aerial Image Classification.
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Author(s)
Xiaoyang, FU
Dale, Patricia
Shuqing, ZHANG
Griffith University Author(s)
Year published
2008
Metadata
Show full item recordAbstract
Coastal wetlands are characterized by complex patterns both in their geomorphic and ecological features. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set ...
View more >Coastal wetlands are characterized by complex patterns both in their geomorphic and ecological features. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm (BP), it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, suchas Bayes maximum-likelihood classifier, VGA classifier and BP-MLP (multi-layer perception) classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.
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View more >Coastal wetlands are characterized by complex patterns both in their geomorphic and ecological features. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm (BP), it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, suchas Bayes maximum-likelihood classifier, VGA classifier and BP-MLP (multi-layer perception) classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.
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Journal Title
Chinese Geographical Science
Volume
18
Issue
2
Publisher URI
Copyright Statement
© 2008 Springer-Verlag. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
Subject
Physical geography and environmental geoscience
Human geography