Classification of Cloud Particle Habits Using Transfer Learning with a Deep Convolutional Neural Network
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| Publicat a: | Atmosphere vol. 16, no. 3 (2025), p. 294 |
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| Autor principal: | |
| Altres autors: | , , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2073-4433 | ||
| 024 | 7 | |a 10.3390/atmos16030294 |2 doi | |
| 035 | |a 3181383453 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231428 |2 nlm | ||
| 100 | 1 | |a Xu, Yefeng |u China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China; <email>yefeng.xu@bistu.edu.cn</email>; Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China; School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China | |
| 245 | 1 | |a Classification of Cloud Particle Habits Using Transfer Learning with a Deep Convolutional Neural Network | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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). | |
| 651 | 4 | |a China | |
| 653 | |a Cloud microphysics | ||
| 653 | |a Automatic classification | ||
| 653 | |a Accuracy | ||
| 653 | |a Habits | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Neural networks | ||
| 653 | |a Cloud particles | ||
| 653 | |a Clouds | ||
| 653 | |a Machine learning | ||
| 653 | |a Crystals | ||
| 653 | |a Radiation | ||
| 653 | |a Transfer learning | ||
| 653 | |a Doppler effect | ||
| 653 | |a Precipitation | ||
| 653 | |a Remote sensing | ||
| 653 | |a Ice crystals | ||
| 653 | |a Atmospheric sciences | ||
| 653 | |a Images | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Jiao, Ruili |u School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China | |
| 700 | 1 | |a Li, Qiubai |u School of Earth Sciences, Yunnan University, Kunming 650091, China; <email>liqiubai@stu.ynu.edu.cn</email>; Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China | |
| 700 | 1 | |a Huang, Minsong |u China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China; <email>yefeng.xu@bistu.edu.cn</email>; Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China; Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Key Laboratory of Cloud-Precipitation Physics and Weather Modification (CPML), China Meteorological Administration, Beijing 100081, China | |
| 773 | 0 | |t Atmosphere |g vol. 16, no. 3 (2025), p. 294 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181383453/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181383453/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181383453/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |