Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 6
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

Viewed

Total article views: 3,011 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,161 774 76 3,011 64 67
  • HTML: 2,161
  • PDF: 774
  • XML: 76
  • Total: 3,011
  • BibTeX: 64
  • EndNote: 67
Views and downloads (calculated since 11 Dec 2019)
Cumulative views and downloads (calculated since 11 Dec 2019)

Viewed (geographical distribution)

Total article views: 3,011 (including HTML, PDF, and XML) Thereof 2,957 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Read more