Skip to main content
SpringerLink
Log in
Book cover

International Conference in Communications, Signal Processing, and Systems

CSPS 2019: Communications, Signal Processing, and Systems pp 1411–1417Cite as

Evaluation on Learning Strategies for Multimodal Ground-Based Cloud Recognition

  • Conference paper
  • First Online:

  • 74 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 571))

Abstract

As a sign of atmospheric processes, clouds play a crucial role in regulating the earth energy balance, redistributing surplus heat and hydrologic cycle. Appropriate recognition method is essential for accurate ground-based cloud classification. This paper evaluates three kinds of learning strategies, i.e., end-to-end method, k-nearest neighbor (KNN) classifier, support vector machine (SVM) for multimodal ground-based cloud recognition. The experimental results demonstrates that SVM is superior to the other methods on multimodal ground-based cloud recognition.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   629.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   799.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   799.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Xiao Y, Cao Z, Zhuo W, Ye L, Zhu L (2016) mCLOUD: a multiview visual feature extraction mechanism for ground-based cloud image categorization. J Atmos Ocean Technol 33:789–801

    Article  Google Scholar 

  2. Luo Q, Meng Y, Liu L, Zhao X, Zhou Z (2018) Cloud classification of ground-based infrared images combining manifold and texture features. Atmos Meas Tech 11:5351–5361

    Article  Google Scholar 

  3. Liu S, Zhang Z, Mei X (2015) Ground-based cloud classification using weighted local binary patterns. J Appl Remote Sens 9:095062

    Article  Google Scholar 

  4. Wang Y, Wang C, Shi C, Xiao B (2019) A selection criterion for the optimal resolution of ground-based remote sensing cloud images for cloud classification. IEEE Trans Geosci Remote Sens 57:1358–1367

    Article  Google Scholar 

  5. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 91–99

    Google Scholar 

  6. Zhang Z, Si T, Liu S (2018) Integration convolutional neural network for person re-identification in camera networks. IEEE Access 6:36887–36896

    Article  Google Scholar 

  7. Zhang Z, Zhang H, Liu S (2019) Coarse-fine convolutional neural network for person re-identification in camera sensor networks. IEEE Access 7:65186–65194

    Article  Google Scholar 

  8. Zhang Z, Wang H, Liu S, Xiao B (2018) Consecutive convolutional activations for scene character recognition. IEEE Access 6:35734–35742

    Article  Google Scholar 

  9. Zhang Z, Wang H, Liu S, Xiao B (2018) Deep contextual stroke pooling for scene character recognition. IEEE Access 6:16454–16463

    Article  Google Scholar 

  10. Shi C, Wang C, Wang Y, Xiao B (2017) Deep convolutional activations-based features for ground-based cloud classification. IEEE Geosci Remote Sens Lett 14:816–820

    Article  Google Scholar 

  11. Zhang J, Liu P, Zhang F, Song Q (2018) CloudNet: ground-based cloud classification with deep convolutional neural network. Geophys Res Lett 45:8665–8672

    Article  Google Scholar 

  12. Ye L, Cao Z, Xiao Y (2017) DeepCloud: ground-based cloud image categorization using deep convolutional features. IEEE Trans Geosci Remote Sens 55:5729–5740

    Article  Google Scholar 

  13. Li M, Liu S, Zhang Z (2019) Dual guided loss for ground-based cloud classification in weather station networks. IEEE Access 7:63081–63088

    Article  Google Scholar 

  14. Liu S, Li M (2018) Deep multimodal fusion for ground-based cloud classification in weather station networks. EURASIP J Wirel Commun Netw 48

    Google Scholar 

  15. Liu S, Li M, Zhang Z, Xiao B, Cao X (2018) Multimodal ground-based cloud classification using joint fusion convolutional neural network. Remote Sens 10:822

    Article  Google Scholar 

  16. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770–778

    Google Scholar 

  17. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE international conference on computer vision, pp 1026–1034

    Google Scholar 

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 1097–1105

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant No. 61501327 and No. 61711530240, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600, the Fund of Tianjin Normal University under Grant No. 135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201800002, the Tianjin Higher Education Creative Team Funds Program, and the Postgraduate Research Practice Project of Tianjing Normal University under Grant No. YZ1260021938.

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China

    Shuang Liu, Mei Li & Zhong Zhang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Shuang Liu, Mei Li & Zhong Zhang

  3. Meteorological Observation Centre, China Meteorological Administration, Beijing, China

    Xiaozhong Cao

Authors
  1. Shuang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaozhong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shuang Liu or Xiaozhong Cao .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

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

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Zhenyu Na

  5. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China

    Min Jia

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, S., Li, M., Zhang, Z., Cao, X. (2020). Evaluation on Learning Strategies for Multimodal Ground-Based Cloud Recognition. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_169

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9409-6_169

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9408-9

  • Online ISBN: 978-981-13-9409-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation