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A Remote Sensing Image Matching Algorithm Based on the Feature Extraction

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Advances in Neural Networks – ISNN 2012 (ISNN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7368))

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Abstract

In this paper, a novel method for remote sensing image matching through mean-shift is proposed. First, state of the improved Mean-shift is reminded. Primary mean-shift algorithm is only based on color feature, but color feature does not apply to the remote sensing images matching. This paper exhibits a method to solve this problem using the gradient direction histogram instead of the color histogram. Secondly, Speeded-Up Robust Features (SURF) is applied to the fine matching. The experimental results show that the improved mean-shift matching algorithm, combining to the surf detector can realize two images matching accurately.

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References

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Author information

Authors and Affiliations

  1. College of Information Science & Engineering, Northeastern University, Shenyang, China

    Chengdong Wu, Chao Song, Dongyue Chen & Xiaosheng Yu

Authors
  1. Chengdong Wu
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  2. Chao Song
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  3. Dongyue Chen
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  4. Xiaosheng Yu
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Editor information

Editors and Affiliations

  1. Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Jun Wang

  2. School of Electrical and Computer Engineering, Oklahoma State University, 74078, Stillwater, OK, USA

    Gary G. Yen

  3. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios M. Polycarpou

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© 2012 Springer-Verlag Berlin Heidelberg

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Wu, C., Song, C., Chen, D., Yu, X. (2012). A Remote Sensing Image Matching Algorithm Based on the Feature Extraction. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_32

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  • DOI: https://doi.org/10.1007/978-3-642-31362-2_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31361-5

  • Online ISBN: 978-3-642-31362-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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