Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 12
Articles | Volume 18, issue 12
https://doi.org/10.5194/amt-18-2781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 12
Research article
 | 
27 Jun 2025
Research article |  | 27 Jun 2025

Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm

Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger

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Short summary
Our study improves ice crystal shape classification, key for understanding weather and climate. By adding unsupervised pre-training before supervised classification, our algorithm reduces manual labeling effort while maintaining high accuracy. It outperforms fully supervised models across datasets of varying sizes and categories, showing strong generalization ability. This method improves ice crystal classification techniques, making it adaptable to different environmental datasets.
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