Abstract: To support the fast recognition of ground targets in remote sensing images, an improved U-Net neural network-based target extraction algorithm is proposed, the classical deep learning neural network U-Net is selected as the backbone network, dense connections in the encoder part is added to alleviate the network degradation problem of Wide-Range Attention Unit （WRAU）and is added to better fuse multi-scale feature channels. Massachusetts and DeepGlobe datasets are evaluated.The experimental results validate that the superiority performance of WRAU-Net compared with that of U-Net, ResUNet, UNetPPL, E-Net, SegNet and other networks. Finally, the future research trends of deep learning in the field of remote sensing image target detection is discussed in the conclusion section.