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Classification of Cloud Particle Habits Using Transfer Learning with a Deep Convolutional Neural Network

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Publicado en:Atmosphere vol. 16, no. 3 (2025), p. 294
Autor principal: Xu, Yefeng
Otros Autores: Jiao, Ruili, Li, Qiubai, Huang, Minsong
Publicado:
MDPI AG
Materias:
China
Cloud microphysics
Automatic classification
Accuracy
Habits
Comparative analysis
Deep learning
Datasets
Classification
Artificial neural networks
Neural networks
Cloud particles
Clouds
Machine learning
Crystals
Radiation
Transfer learning
Doppler effect
Precipitation
Remote sensing
Ice crystals
Atmospheric sciences
Images
Methods
Algorithms
Acceso en línea:Citation/Abstract
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Resumen:The habits of cloud particles are a significant factor impacting microphysical processes in clouds. The accurate identification of cloud particle shapes within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we established a cloud particle image dataset encompassing nine distinct habit categories, totaling 8100 images. These images were captured using three probes with varying resolutions: the Cloud Particle Imager (CPI), the Two-Dimensional Stereo Probe (2D-S), and the High-Volume Precipitation Spectrometer (HVPS). Furthermore, this study performs a comparative analysis of ten different transfer learning (TL) models based on this dataset. It was found that the VGG-16 model exhibits the highest classification accuracy, reaching 97.90%. This model also demonstrates the highest recall, precision, and F1 measure. The results indicate that the VGG-16 model can reliably classify the shapes of ice crystal particles measured by both line scan imagers (2D-S, HVPS) and an area scan imager (CPI).
ISSN:2073-4433
DOI:10.3390/atmos16030294
Fuente:Publicly Available Content Database