Aiming at the problems of insufficient attention to detailed information such as small water bodies and water edges in remote sensing image water body extraction research, as well as poor water body connectivity, this paper proposes a densely connected U-shaped network ( ASPP-SCBAM-DenseUnet ) based on improved atrous spatial pyramid pooling and stochastic convolutional block attention module. In this paper, the Dense Block block is used to form the encoder and decoder parts of Unet, and the SCBAM attention mechanism is introduced to reduce noise interference and improve the accuracy of water boundary segmentation. Secondly, the ASPP_SCBAM module is added to set different atrous rates, expand the receptive field, and combine the shallow and deep features of small water bodies to compensate for the feature loss caused by the sampling process. Finally, the network is trained by combining the joint loss function of Dice coefficient and pixel-level binary cross entropy to effectively deal with the unbalanced data set caused by small water bodies. This not only ensures the accuracy of segmentation, but also produces a smoother and more continuous segmentation boundary, thus preventing the model from overfitting or over-refinement. The experimental results show that the scores of pixel accuracy, recall and F1-score extracted by ASPP-SCBAM-DenseUnet network model are 94.19%, 94.29% and 95.15%, respectively, and the scores of frequency weighted intersection over union and mean intersection over union are 89.02% and 88.63%, respectively, which are significantly better than those of semantic segmentation networks such as Unet and Linknet. At the same time, it reduces the misclassification and omission of water bodies, optimizes the edge details of water bodies, and improves the identification of small water bodies and the connectivity of water bodies.