Unsupervised classification of snowflake images using a generative adversarial network and <i>K</i>-medoids classification

Leinonen, Jussi; Berne, Alexis

doi:https://doi.org/10.5194/amt-13-2949-2020

Articles | Volume 13, issue 6

https://doi.org/10.5194/amt-13-2949-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-2949-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 6

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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Final revised paper (published on 05 Jun 2020)
Preprint (discussion started on 11 Dec 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comments on amt-2019-440', Anonymous Referee #1, 14 Jan 2020
- AC1: 'Response to Referee #1', Jussi Leinonen, 15 Apr 2020
RC2: 'Review AMT-2019-440', Anonymous Referee #2, 30 Mar 2020
- AC2: 'Response to Referee #2', Jussi Leinonen, 15 Apr 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Jussi Leinonen on behalf of the Authors (15 Apr 2020) Author's response Manuscript

ED: Publish as is (30 Apr 2020) by Daqing Yang

AR by Jussi Leinonen on behalf of the Authors (01 May 2020)

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.