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 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. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The proposed method for ground-based visible image classification based on TCNN combines the abilities of DL and TL. By using a sample database containing all ten cloud types and expanding it four-fold through enhancement processing, the method achieved a recognition accuracy of 92.3% for all ten ground-based cloud types after layer-by-layer fine-tuning to determine the optimal method.