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Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

FU Xiaoyang P E R Dale ZHANG Shuqing

FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. 中国地理科学, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
引用本文: FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. 中国地理科学, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. Chinese Geographical Science, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
Citation: FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. Chinese Geographical Science, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x

Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

doi: 10.1007/s11769-008-0162-x
详细信息
    通讯作者:

    FU Xiaoyang. E-mail: dvndavidfu@vip.163.com

Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

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出版历程
  • 收稿日期:  2007-10-09
  • 修回日期:  2008-03-30
  • 刊出日期:  2008-06-20

Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

doi: 10.1007/s11769-008-0162-x
    通讯作者: FU Xiaoyang. E-mail: dvndavidfu@vip.163.com

摘要: 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 algo-rithm (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, such as 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.

English Abstract

FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. 中国地理科学, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
引用本文: FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. 中国地理科学, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. Chinese Geographical Science, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x
Citation: FU Xiaoyang, P E R Dale, ZHANG Shuqing. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification[J]. Chinese Geographical Science, 2008, 18(2): 162-170. doi: 10.1007/s11769-008-0162-x

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