Intelligent classification of ground-based visible cloud images using a transfer convolutional neural network and fine-tuning

Here a classification method for ground-based visible images is proposed based on a transfer convolutional neural network (TCNN). This approach combines the ability of deep learning (DL) and transfer learning (TL). A sample database containing all ten cloud types was used; this database was expanded...

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Bibliographic Details
Published in:Optics express 2021-12, Vol.29 (25), p.41176
Main Authors: Wang, Min, Zhuang, Zhihao, Wang, Kang, Zhou, Shudao, Liu, Zhanhua
Format: Article
Language:English
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Summary:Here a classification method for ground-based visible images is proposed based on a transfer convolutional neural network (TCNN). This approach combines the ability of deep learning (DL) and transfer learning (TL). A sample database containing all ten cloud types was used; this database was expanded four-fold using enhancement processing. AlexNet was chosen as the basic convolutional neural network (CNN), with the ImageNet database being used for pre-transfer. The optimal method, once determined by layer-by-layer fine-tuning, was used to test the classification effects for ten cloud types. The proposed method achieved 92.3% recognition accuracy for all ten ground-based cloud types.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.442455