Accurately acquiring cloud information through cloud images segmentation is of great importance for weather forecasting, environmental monitoring, sites selection of observatory and analysis of climate evolution. In this paper, a cloud segmentation method based on deep learning, called CloudU-Netv2, is proposed to segment daytime and nighttime ground-based cloud images. The CloudU-Netv2 includes encoder, dual attention modules and decoder. The contributions in this paper are four folds as follows. Firstly, it replaces the ‘upsampling’ in CloudU-Net with ‘bilinear upsampling’. Secondly, position and channel attention modules are added to the structure to improve the discrimination ability of features’ representation. Thirdly, it chooses rectified Adam as the optimizer in the CloudU-Netv2 structure. Finally, we conduct ablation experiments on the key components of CloudU-Netv2 and compare with the existing four advanced methods using six evaluation metrics. Results show that the key components of the model play the pivotal role in improving the segmentation performance, and the proposed CloudU-Netv2 has the best segmentation performance for daytime and nighttime ground-based cloud images compared with four other methods.

通过云图像分割准确地获取云信息对于天气预报，环境监测，天文台选址和气候演变分析具有重要意义。本文提出了一种基于深度学习的云分割方法CloudU-Netv2，用于对白天和晚上的地面云图像进行分割。CloudU-Netv2包括编码器，双重注意模块和解码器。本文的贡献有以下四个方面。首先，它将CloudU-Net中的“上采样”替换为“双线性上采样”。其次，将位置和通道注意模块添加到结构中，以提高特征表示的识别能力。第三，它选择经过整流的Adam作为CloudU-Netv2结构中的优化器。最后，我们对CloudU-Netv2的关键组件进行了消融实验，并使用六个评估指标与现有的四种先进方法进行了比较。结果表明，该模型的关键组成部分在提高分割性能方面起着关键作用，与其他四种方法相比，提出的CloudU-Netv2在白天和夜间的地面云图像中具有最佳的分割性能。