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International Conference on Neural Information Processing

ICONIP 2006: Neural Information Processing pp 314–323Cite as

Remote Sensing Image Fusion Based on Adaptive RBF Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4233))

Abstract

With the availability of multi-sensor and multi-frequency image data from operational observation satellites, the fusion of image data has become an important tool in remote sensing image evaluation and segmentation. This paper presents a novel Radius Basis Function (RBF) neural network with some distinctive training strategies, which can integrate multiple information sources efficiently and exploit the potential advantages of each feature. Multi-scale features extracted from remote sensing images are evaluated adaptively and used for segmentation. Experimental results obtained on artificial and real data are both presented which demonstrate the effectiveness of our proposal.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Fudan University, Shanghai, 200433, China

    Yun Wen Chen & Bo Yu Li

Authors
  1. Yun Wen Chen
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  2. Bo Yu Li
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Chen, Y.W., Li, B.Y. (2006). Remote Sensing Image Fusion Based on Adaptive RBF Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_35

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  • DOI: https://doi.org/10.1007/11893257_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46481-5

  • Online ISBN: 978-3-540-46482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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