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International Symposium on Neural Networks

ISNN 2004: Advances in Neural Networks – ISNN 2004 pp 774–779Cite as

Extracting Target Information in Multispectral Images Using a Modified KPCA Approach

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

Abstract

In this paper, a modified kernel principal component analysis (KPCA) approach is proposed to extract target information in multispectral images. The advantage of this method is to be able to extract target information with fairly small computation complexity compared to the standard KPCA when a large number of input samples need to be processed. Finally, some experimental results demonstrate that our proposed approach is effective and efficient for analyzing and interpreting the multispectral images.

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References

  1. Chitroub, S., Houacine, A., Sansal, B.: Unsupervised Learning Rules for POLSAR Images Analysis. In: Proceedings of the12th IEEE Workshop on Neural Networks for Signal Processing, pp. 567–576 (2002)

    Google Scholar 

  2. Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue. Neural Comput. 10, 1299–1319 (1998)

    Article  Google Scholar 

  3. Oja, E.: A Simplified Neuron Model as A Principal Component Analyzer. J. Math. Biology 15, 267–273 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  4. Diamantaras, K.I., Kung, S.Y.: Principal Component Neural Networks. Wiley, New York (1996)

    MATH  Google Scholar 

  5. Kim, K.I., Franz, M.O., Schölkopf, B.: Image Modeling Based on Kernel Principal Component Analysis, http://www.kyb.tuebingen.mpg.de/publication.html

  6. Schölkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Müller, K.-R., Smola, A.J.: Input Space VS. Feature Space in Kernel-Based Methods. IEEE Trans. Neural Networks 5, 1000–1017 (1999)

    Article  Google Scholar 

  7. Ready, P., Wintz, P.: Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery. IEEE Trans. Communications 21(10), 1123–1131 (1973)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Computing Group, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O.Box 1130, Hefei, Anhui, 230031, China

    Zhan-Li Sun & De-Shuang Huang

  2. Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China

    Zhan-Li Sun

Authors
  1. Zhan-Li Sun
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  2. De-Shuang Huang
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Cite this paper

Sun, ZL., Huang, DS. (2004). Extracting Target Information in Multispectral Images Using a Modified KPCA Approach. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_127

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_127

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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