Adaptive enhanced swin transformer with U-net for remote sensing image segmentation

•In this paper, a UAV remote sensing segmentation method based on CNN and transformer is proposed. On the basis of the U-Net structure, we introduce a CNN transformer hybrid encoder and a symmetrical CNN decoder, which can effectively extract and utilize the global and local semantic information for obtaining the segmentation image accurately and reduce the calculation of transformer. Besides, we construct an adaptive multiscale transformer module and strengthen the multi-head self-attention in it for boosting the performance of AESwin-UNet. Experimental results on two UAV remote sensing datasets show that our AESwin-UNet has excellent performance. Our contributions can be summarized as:.•Based on hybrid CNN-Transformer, a U-shaped encoder-decoder model with skip connections is proposed, which realizes pixel-level segmentation prediction by fusing local and global feature, while reducing the scale of pre-training.•An enhanced swin transformer block with an attention module is constructed, which enhances the extraction of the effective features by reducing the redundancy in MHSA.•A deformable adaptive patch merging layer is proposed to assign appropriate receptive fields to different targets while achieving down-sampling.