Improved RepVGG ground-based cloud image classification with attention convolution

Abstract

Atmospheric clouds greatly impact Earth's radiation, hydrological cycle, and climate change. Accurate automatic recognition of cloud shape based on a ground-based cloud image is helpful for analyzing solar irradiance, water vapor content, and atmospheric motion and then predicting photovoltaic power, weather trends, and severe weather changes. However, the appearance of clouds is changeable and diverse, and their classification is still challenging. In recent years, convolution neural networks (CNNs) have made great progress in ground-based cloud image classification. However, traditional CNNs poorly associate long-distance clouds, making the extraction of global features of cloud images quite problematic. This study attempts to mitigate this problem by elaborating on a ground-based cloud image classification method based on the improved RepVGG convolution neural network and attention mechanism. Firstly, the proposed method increases the RepVGG residual branch and obtains more local detail features of cloud images through small convolution kernels. Secondly, an improved channel attention module is embedded after the residual branch fusion, effectively extracting the global features of cloud images. Finally, the linear classifier is used to classify the ground cloud images. Finally, the warm-up method is applied to optimize the learning rate in the training stage of the proposed method, making it lightweight in the inference stage and thus avoiding overfitting and accelerating the model's convergence. The proposed method is validated on the multimodal ground-based cloud dataset (MGCD) and the ground-based remote sensing cloud database (GRSCD) containing seven cloud categories, with the respective classification accuracy rate values of 98.15% and 98.07% outperforming those of the 10 most advanced methods used as the reference. The results obtained are considered instrumental in ground-based cloud image classification.