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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 Jul 2019

Submitted as: research article | 15 Jul 2019

Review status
A revised version of this preprint is currently under review for the journal AMT.

A Convolutional Neural Network for Classifying Cloud Particles Recorded by Imaging Probes

Georgios Touloupas1,*, Annika Lauber2,*, Jan Henneberger2, Alexander Beck2, Thomas Hofmann1, and Aurélien Lucchi1 Georgios Touloupas et al.
  • 1ETH Zurich, Institute of Machine Learning
  • 2ETH Zurich, Institute for Atmospheric and Climate Science
  • *These authors contributed equally to this work

Abstract. During typical field campaigns, millions of cloud particle images are captured with imaging probes. Our interest lies in classifying these particles in order to compute the statistics needed for understanding clouds. Given the large volume of collected data, this raises the need for an automated classification approach. Traditional classification methods that require extracting features manually (e.g. decision trees and support vector machines) show reasonable performance when trained and tested on data coming from a unique dataset. However, they often have difficulties to generalize to test sets coming from other datasets where the distribution of the features might be significantly different. In practice, we found that each new dataset requires labeling a huge amount of data by hand using those methods. Convolutional neural networks have the potential to overcome this problem due to their ability to learn complex non-linear models directly from the images instead of pre-engineered features, as well as by relying on powerful regularization techniques. We show empirically that a convolutional neural network trained on cloud particles from holographic imagers generalizes well to unseen datasets. Moreover, fine-tuning the same network with a small number (256) of training images improves the classification accuracy. Thus, the automated classification with a convolutional neural network not only reduces the hand-labeling effort for new datasets but is also no longer the main error source for the classification of small particles.

Georgios Touloupas et al.

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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Georgios Touloupas et al.

Georgios Touloupas et al.


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Latest update: 22 Feb 2020
Publications Copernicus
Short summary
Images of cloud particles give important information to improve our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals needs to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Images of cloud particles give important information to improve our understanding of...