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Hyperspectral Image Classification Using Extreme Learning Machine and Conditional Random Field

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Extreme Learning Machines 2013: Algorithms and Applications

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 16))

Abstract

Recent studies show that extreme learning machine (ELM) is a suitable, effective, and less time-consuming classifier with a wide range of applications. This chapter addresses the application of ELM to the remotely sensed hyperspectral image classification. In this chapter, the proposed hyperspectral image classification method consists of three steps: First, a semi-supervised feature extract algorithm is used for dimensionality reduction; Second, ELM is taken as a classifier; Finally, conditional random field (CRF) is taken to smooth the result of ELM classifier, where the probability estimation over each class obtained by ELM is used as unary potential function of CRF. The experimental results show that the proposed hyperspectral image classification method using both ELM and CRF achieves good classification performance on two real hyperspectral data sets in comparison to the methods using SVM and CRF.

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Notes

  1. 1.

    https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html

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Acknowledgments

We are grateful for financial support from the National Nature Science Foundation of China under Grant No. 61101202 and the National Technology Research and Development Program of China under Grant No. 2012AA01A510.

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Authors and Affiliations

  1. College of Command Information Systems, PLA University of Science and Technology, Nanjing, 210007, China

    Yanyan Zhang, Dong Li & Zhisong Pan

  2. Institute of Communications Engineering, PLA University of Science and Technology, Nanjing, 210007, China

    Lu Yu

Authors
  1. Yanyan Zhang
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  2. Lu Yu
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  3. Dong Li
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  4. Zhisong Pan
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Corresponding author

Correspondence to Zhisong Pan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Fuchen Sun

  2. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of (South Korea)

    Kar-Ann Toh

  3. Department of Computer Science and Artificial Intelligence, Universidad Del Pais Vasco, San Sebastian, Spain

    Manuel Grana Romay

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

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Zhang, Y., Yu, L., Li, D., Pan, Z. (2014). Hyperspectral Image Classification Using Extreme Learning Machine and Conditional Random Field. In: Sun, F., Toh, KA., Romay, M., Mao, K. (eds) Extreme Learning Machines 2013: Algorithms and Applications. Adaptation, Learning, and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-04741-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-04741-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04740-9

  • Online ISBN: 978-3-319-04741-6

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