Virtual Samples for Cloud Classification via Supervised Learning

  • Shuang Liu
  • Mei Li
  • Zhong Zhang
  • Mingzhu Shi
  • Xiaozhong CaoEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


Convolutional neural networks (CNNs) have been widely used in image classification task, which is based on the huge amount of image samples. However, the insufficiency of cloud sample numbers brings obstacles to classify clouds using CNNs. In this paper, we propose to apply Wasserstein generative adversarial network (WGAN) to generate virtual cloud samples via supervised learning. Afterward, we fine-tune a deep CNN model to evaluate the classification performance under different number of virtual cloud samples. The experimental results demonstrate the feasibility of the proposed method.


Convolutional neural networks Generative model Discriminative model Cloud classification 



This work was supported by National Natural Science Foundation of China under Grant No. 61501327, No. 61711530240, and No. 61501328, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600 and No. 15JCQNJC01700, the Fund of Tianjin Normal University under Grant No.135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201700001 and No. 201800002, the China Scholarship Council No. 201708120039 and No. 201708120040, and the Tianjin Higher Education Creative Team Funds Program.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shuang Liu
    • 1
    • 2
  • Mei Li
    • 1
    • 2
  • Zhong Zhang
    • 1
    • 2
  • Mingzhu Shi
    • 1
    • 2
  • Xiaozhong Cao
    • 3
    Email author
  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina
  2. 2.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina
  3. 3.Meteorological Observation CentreChina Meteorological AdministrationBeijingChina

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