Road Segmentation from Satellite Imagery Based on an Improved SAM Model

Abstract. Road network is an important infrastructure of urban spatial structure and traffic system. Its accurate acquisition is of great significance for urban traffic analysis, automatic driving map construction and disaster emergency response. With the wide acquisition of high-resolution remote sensing images, automatic extraction of road masks from remote sensing images has become an important research direction in the field of remote sensing image understanding. However, the existing deep learning methods still face the problems of obvious modal differences and insufficient modeling of road structure continuity in remote sensing scenes. To solve the above problems, this paper proposes a remote sensing image road segmentation model LR-SAM based on SAM (Segment Anything Model). In this model, the LoRA (Low Rank Adaptation) fine-tuning strategy is introduced to achieve efficient parameter updating, and the MS multi-scale feature interaction module is designed in the coding phase to enhance the expression ability of the linear structure and fine-grained information of the road. At the same time, the original prompt encoder is removed and a lightweight AD decoder is constructed to achieve multi-scale feature fusion. In the reasoning stage, TTA (Test Time Augmentation) strategy is introduced to improve the stability and segmentation accuracy of the model. Experimental results based on CHN6-CUG and SATMTB datasets show that the proposed method achieves 97.20% and 85.06% mIoU and 96.67% and 84.94% F1-score, respectively, which is significantly better than the mainstream road segmentation methods, and verifies the effectiveness of the proposed improvement points.