There are two main methods of traditional water body extraction: a method based on spectral information and a method based on classification. Traditional water body extraction methods based on spectral information fail to take into account features such as water body shape, internal texture, water body size, and adjacent relations of water body. Also, there is a common phenomenon of “same object with different spectra and same spectrum with different objects”, which could result in low accuracy of water body extraction. Thus, the traditional methods that design features based on classification to extract water body is complex and impossible to capture the deep information of water body features. This paper proposed an improved U-Net network semantic segmentation method, which uses the de-encoding structure of the classic U-Net network to improve the network: ① Use the VGG network to shrink the path and increase the depth of the network to extract deep features of the water; ② Strengthen the low-dimensional feature information in the expansion path, fuse the feature map on the next layer of the shrinking feature pyramid with the feature map on the corresponding expansion path in the next layer, and enhance the model's low-dimensional feature information to improve the classification accuracy of the model; and ③ The Conditional Random Feld (CRF) was introduced in the post-classification process to refine the segmentation results and improve the segmentation accuracy. In the study of Qingdao area, SegNet, classic U-Net network, and improved U-Net network were selected as controlled experiments while maintaining the same training set, validation set, and test set. The test results show that the improved U-Net network structure performed better than SegNet and classic U-Net networks in terms of IoU, accuracy rate and Kappa coefficient. Compared with SegNet, the three indicators increased by 10.5%, 12.3%, and 0.14, respectively. Compared with the results of the classic U-Net network, each indicator increased by 5.8%, 4.4% and 0.05, respectively. The results demonstrate the effectiveness of the improved method in this paper. In addition, the method proposed in this paper has more advantages than the other two networks in the extraction of small targets in the test area, the completeness of water body extraction, the distinction between shadows and water bodies, and the accuracy of boundary segmentation. In order to verify the space-time scalability of the model, this paper chose western Qingdao and Xining, Qinghai as the verification areas. The verification results show that the water body extraction was good for areas similar to the geographical environment of the experimental area, and the effect of water body extraction needs to be further improved in places that differ greatly from the geographical environment of the experimental area. In general, the improved U-Net network can effectively achieve the task of water extraction.