CloudFU-Net: A Fine-Grained Segmentation Method for Ground-Based Cloud Images Based on an Improved Encoder–Decoder Structure

The segmentation of ground-based cloud image is a crucial aspect of ground-based cloud observation, with significant implications for meteorological forecasting, photovoltaic power prediction, and other related tasks. At present, the proposed method of ground-based cloud image segmentation only separates cloud from the sky background without further classifying the cloud categories. Clouds have rich fine-grained semantic features, and different types of clouds have different effects on solar irradiance, which in turn has different effects on photovoltaic power. In this paper, a fine-grained segmentation method for ground-based cloud images is proposed, which is based on an improved encoder-decoder structure named CloudFU-Net. Firstly, a ground-based cloud image fine-grained segmentation data set for Photovoltaic power prediction is constructed, and the clouds are divided into five categories with different colors under the guidance of meteorologists; Secondly, Selective Kernel (SK) is introduced in CloudFU-Net encoder to better capture cloud of different sizes. Then, Parallel dilated convolution model (PDCM) is proposed to segment small target clouds more accurately. Finally, a Content-Aware ReAssembly of Features(CARAFE) is introduced into CloudFU-Net decoder to replace the original interpolating upsampling to better recover fine-grained semantic features. Lastly, the experimental results show that the proposed CloudFU-Net has the best segmentation performance compared with other segmentation models, with Miou reaching 61.9%, which can efficiently segment different cloud genera and lay a solid foundation for accurate prediction of photovoltaic power.