The pixel-level sea-land segmentation of remote sensing images is a basic work for coastline extraction. Owing to the dynamic changes in the coastline, obtaining accurate coastline marker datasets is difficult. In this study, Google Aerial Photo-Maps-paired samples were used to construct a paired dataset after the sea-land binarization processing of Google Maps. Thus, we proposed the dual attention mechanism-cycle generative adversarial network (CycleGAN) based on the CycleGAN model to solve the problem of fewer samples in the new dataset. The new model fully considers the structural similarity between remote sensing images and sea-land binarized images, improves cycle consistency loss, and designs both channel and spatial attention modules to highlight salient features and regions to enhance the model’s performance in small feature learning ability under sample training. Furthermore, we applied three evaluation indicators, i.e., mean square error, mean pixel accuracy, and mean intersection over union (MIoU), and compared our experimental results to those of the full convolutional neural network and DeepLab models under multiple-scale dataset training. Results show that the improved model conversion of the sea-land binarized images is more consistent with the true value images and the MIoU values are increased by at least 7% and 6%, respectively, verifying the effectiveness and feasibility of the proposed method.