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Sea fog is a common weather phenomenon at sea. It will reduce visibility at sea and pose a significant threat to maritime traffic and other operations. Traditional sea fog detection algorithms using satellite remote sensing have low accuracy, poor portability, and low automation. Although some existing deep learning-based sea fog monitoring algorithms have been improved, they do not consider the spectral characteristics of sea fog in different channels, and the accuracy of sea fog monitoring is not high, especially in edge recognition. In order to improve the accuracy of sea fog detection, a daytime sea fog detection method was proposed, which based on multi-scale feature fusion of generate adversarial network (GAN) under attention mechanism. Firstly, according to the spectral response of sea fog in different imaging channels of meteorological satellite, the satellite cloud images of different imaging channels that can reflect the characteristics of sea fog are selected as the input of the network. Meanwhile, in order to make the network better focus on the significant imaging channels under multi-channel input, the channel attention mechanism is introduced to measure the weights of different input channels. Then, in order to solve the problem of the loss detail features of the cloud image caused by the pooling operation of the traditional deep network, a multi-scale feature fusion mechanism is adopted to fuse the feature maps of different levels of the network to obtain the multi-scale features of the sea fog. Finally, in view of the difficulty of traditional methods to accurately describe the edge of sea fog, the generation network for sea fog detection is supervised by adversarial network, so as to accurately define the edge of sea fog and reduce the false alarm rate. This article takes the Yellow Sea and the Bohai Sea as the research area. Since March to June each year is the period of high incidence of sea fog in the Yellow Sea and the Bohai Sea, we produce a data set based on the weather satellite monitoring report of the National Meteorological Center from March to June 2017-2020. After the model training in this article, in terms of quantitative indicators of sea fog detection, probability of detection (POD), critical success index (CSI) and false positive rate (FAR) of our method are 90.5%, 81.28% and 10.86% respectively, which are better than other methods. The experimental results show that the method in this paper can effectively improve the accuracy of sea fog identification, which is of great significance for marine vessel navigation, fishery production, national defense and military affairs.