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Identification of Remote Sensing Image of Adverse Geological Body Based on Classification

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

Identification of interested landmark is a hot topic in the field of remote sensing. Taking QuickBird as an example, this paper focuses on the typical adverse geological phenomenon, such as desert, saltmarsh, gobi, lakes, etc., in Yuli Rob Village of Xinjiang Province. Three classification methods, i.e., extreme learning machine, SVM algorithm, and K-means algorithm, are used for classification and recognition of remote sensing image. The image recognition rate and accuracy are analyzed. Experimental results and comparison analysis indicate that extreme learning machine algorithm, SVM algorithm and K-means algorithm in general is not significant. The SVM algorithm for image continuity provides better results. The extreme learning machine obtains classification results, yet it is easy to fall into local optimum.

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Xiang Li

  2. School of Engineering, China University of Geosciences, Wuhan, 430074, China

    Hao Zhang

Authors
  1. Xiang Li
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  2. Hao Zhang
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Corresponding author

Correspondence to Hao Zhang .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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© 2015 Springer-Verlag Berlin Heidelberg

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Li, X., Zhang, H. (2015). Identification of Remote Sensing Image of Adverse Geological Body Based on Classification. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_21

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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