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  • 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)
    Dale, Patricia E.
    Year published
    2008
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    Abstract
    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 ...
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    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
    http://www.springer.com/geography/journal/11769
    DOI
    https://doi.org/10.1007/s11769-008-0162-x
    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
    Publication URI
    http://hdl.handle.net/10072/22542
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    • Journal articles

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