Abstract: Clouds constantly contribute significantly to climate change in addition to having a short-term impact on local temperatures.To study local cloud details,the ground-based observation is used caused by its ability of cloud image capture in high temporal and spatial resolution.The research on automatic identification of ground-based clouds is primarily focused on two areas:cloud classification and cloud detection.Traditionally,both of them are regarded as separate and unrelated tasks.Cloud classification are independent of the segmentation,and most segmentation techniques focus on binary segmentation.This making it difficult to segment regions by different cloud types when the cloud image contains multiple classes of clouds.To address this problem,this paper proposes a semantic segmentation method based on deep learning for the combination of two tasks.First,it constructs the fround-based cloud image semantic segmentation(GBCSS) dataset,which contains 3000 cloud images with a total of 11 types.All images are resized to a square format of 256×256 pixels.Then,an improved scheme based on U-shaped neural networks is designed as the semantic segmentation model for ground-based cloud images.The pyramid pooling module is combined for extracting and aggregating image features at different scales.This module improves the network’s ability to obtain global information.The developed network UNet-PPM achieves 91.5% pixel accuracy on average on the test set after being trained and assessed on GBCSS.Our suggested enhanced method outperforms the U-Net,Deeplabv3+,DANet and BiSeNetv2 in terms of pixel accuracy.Experiment results show that the pyramid pooling module contributes a lot to extract cloud contour features and restrain the overfitting problem.Our work show the feasibility of semantic segmentation application in cloud image automatic observation.

Key words: Ground-based cloud image, Semantic segmentation, Cloud image dataset, Fully convolutional network, Pyramid pooling module