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

Viewed

Total article views: 2,497 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,726 715 56 2,497 47 49
  • HTML: 1,726
  • PDF: 715
  • XML: 56
  • Total: 2,497
  • BibTeX: 47
  • EndNote: 49
Views and downloads (calculated since 11 Dec 2019)
Cumulative views and downloads (calculated since 11 Dec 2019)

Viewed (geographical distribution)

Total article views: 2,497 (including HTML, PDF, and XML) Thereof 2,451 with geography defined and 46 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Mar 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.