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Evaluation Embedding Features for Ground-Based Cloud Classification

  • Zhong ZhangEmail author
  • Donghong Li
  • Shuang Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

Ground-based cloud classification plays a vital important role in meteorological research. However, the existing methods perform well confined to one weather station. In this paper, we present a detailed introduction of two representative embedding features for ground-based cloud classification in various weather stations. The features are learned from the metric learning and the convolutional neural network (CNN), respectively. The two kinds of features are evaluated on two weather stations.

Keywords

Ground-based cloud classification Embedding features Metric learning Convolutional neural networks 

Notes

Acknowledgments

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 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

  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina
  2. 2.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina

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