Abstract: Aiming at the problems of missed and misjudgment of the road segment in optical remote sensing image with complex background and rich feature information， this paper puts forward a method of remote sensing image road segmentation based on CA-TransUNet. The semantic segmentation network TransUNet with multi-head self-attention is taken as the benchmark， and the void space pyramid pooling is integrated into the feature extraction module to obtain the feature maps of different horizons. Through the integration of the information of each channel， the extraction of multi-scale features is enhanced. A hybrid attention mechanism is added to the cascaded upsampling module to reduce the loss of process details， suppress the attention to irrelevant boundary information， and enhance the road features. The Dice loss function and binary cross-entropy loss are selected to optimize the road segment of optical remote sensing images more accurately. Experimental results show that the proposed method achieves 56.33% IoU value and 71.32% F1 index on DeepGlobe dataset， and the accuracy is up to 97.32%， which is higher than other classical road segmentation algorithms in remote sensing images. The improved algorithm can effectively segment the remote sensing images with complex surrounding background， obstructed by obstacles and narrow roads.

Key words: remote sensing image, road segmentation, self-attention, atrous spatial pyramid pooling, attention mechanism