Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-2949-2020
https://doi.org/10.5194/amt-13-2949-2020
Research article
 | 
05 Jun 2020
Research article |  | 05 Jun 2020

Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification

Jussi Leinonen and Alexis Berne

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Cited articles

Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein GAN, arXiv [preprint], arXiv1701.07875, 6 December 2017. a
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P.: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, in: Advances in Neural Information Processing Systems 29, edited by: Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I., and Garnett, R., Curran Associates, Inc., 2172–2180, available at: https://papers.nips.cc/paper/6399-infogan-interpretable- representation-learning-by-information-maximizing-generative-adversarial-nets.pdf (last access: 26 May 2020), 2016. a, b
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a
Del Guasta, M., Morandi, M., Stefanutti, L., Brechet, J., and Piquad, J.: One year of cloud lidar data from Dumont d'Urville (Antarctica): 1. General overview of geometrical and optical properties, J. Geophys. Res., 98, 18575–18587, https://doi.org/10.1029/93JD01476, 1993. a
Donahue, J. and Simonyan, K.: Large Scale Adversarial Representation Learning, arXiv [preprint], arXiv1907.02544, 5 November 2019. a, b
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Short summary
The appearance of snowflakes provides a signature of the atmospheric processes that created them. To get this information from large numbers of snowflake images, automated analysis using computer image recognition is needed. In this work, we use a neural network that learns the structure of the snowflake images to divide a snowflake dataset into classes corresponding to different sizes and structures. Unlike with most comparable methods, only minimal input from a human expert is needed.