CloudRaednet: residual attention-based encoder–decoder network for ground-based cloud images segmentation in nychthemeron

Abstract

Obtaining accurate cloudage information through ground-based cloud observation is of great significance to astronomical telescope observatory site selection. This paper proposes a residual attention-based encoder–decoder network (CloudRAEDNet) for ground-based cloud image segmentation in nychthemeron. CloudRAEDNet uses ImageNet pre-trained ResNet50 as the encoder backbone network, which reduces the number of network training. The network decoder introduces residual modules to solve the problem of network degradation caused by the increase in the number of network layers. CloudRAEDNet connects encoder and decoder through attention gates to suppress the features of irrelevant areas and automatically focus on areas with prominent features. In addition, the segmentation performance of the network is further improved by the ranger optimizer. The comparative experimental results show that CloudRAEDNet can segment the local details of the ground-based cloud images more finely without increasing the time complexity. Compared with CloudSegNet, EFCN, CloudU-Net and CloudU-Netv2, CloudRAEDNet has the best segmentation performance. The results of ablation experiments show that the attention module contributes the most to CloudRAEDNet and the residual module contributes the least to CloudRAEDNet. In addition, the pre-training and Ranger optimizer also contribute to improving the segmentation performance of CloudRAEDNet.