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A Terrain Classification Method for POLSAR Images Based on Modified Scattering Parameters

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E-Learning and Games (Edutainment 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11462))

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Abstract

In this paper, improved scattering parameters of polarimetric synthetic aperture radar (POLSAR) image based on spatial information and Bayes rule is proposed. The spatial information of scattering parameters is introduced by using an adaptive weight window. Bayes rule is used to improve the performance of the scattering parameters. Experiments on real AIRSAR L-band fully POLSAR data are carried out, and the efficacy of the improved scattering parameters is verified.

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References

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Author information

Authors and Affiliations

  1. School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, People’s Republic of China

    Shuang Zhang & Lu Wang

  2. ZTE Trunking Technology Corporation, Beijing, People’s Republic of China

    Xiangchuan Yu

  3. China Academy of Electronic and Information Technology, Beijing, People’s Republic of China

    Bo Chen

Authors
  1. Shuang Zhang
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  2. Lu Wang
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  3. Xiangchuan Yu
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  4. Bo Chen
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Corresponding author

Correspondence to Shuang Zhang .

Editor information

Editors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Abdennour El Rhalibi

  2. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  3. Xi’an University of Technology, Xi’an, China

    Haiyan Jin

  4. Hangzhou Normal University, Hangzhou, China

    Dandan Ding

  5. Pontificia Universidad Javeriana, Cali, Colombia

    Andres A. Navarro-Newball

  6. Xi’an University of Technology, Xi’an, China

    Yinghui Wang

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Cite this paper

Zhang, S., Wang, L., Yu, X., Chen, B. (2019). A Terrain Classification Method for POLSAR Images Based on Modified Scattering Parameters. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_21

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  • DOI: https://doi.org/10.1007/978-3-030-23712-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23711-0

  • Online ISBN: 978-3-030-23712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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