Cloud is the most uncertain factor in the climate system and has a huge impact on climate change. Therefore, the study of changes in cloudiness is of great importance for understanding climate and climate change. Cloud detection is also an important research area in satellite remote sensing image pre-processing. But cloud detection is a difficult task due to various noise disturbances in the remote sensing data itself, as well as factors such as ice and snow on the ground. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in methods such as image processing and classification. In this study, we use the modified U-Net architecture that introduces the attention mechanism for cloud detection. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method, especially in snowy areas and other areas covered by bright non-cloud objects. The effectiveness of this method makes it a great potential for other optical image processing as well.