Road-SAM: Adapting the Segment Anything Model to Road Extraction From Large Very-High-Resolution Optical Remote Sensing Images
Wenqing Feng, Fangli Guan, Chenhao Sun, Wei Xu
Abstract
We propose road-segment anything model (SAM), a universal model for extracting roads from large, very-high-resolution (VHR), optical, remote sensing (RS) images. Unlike previous methods, Road-SAM builds upon the foundation of the SAM, a large-scale image-segmentation model, to explore a new paradigm for customizable road extraction (RE). Within the framework, we introduce three variants that allow for flexible insertion of adapters at different positions within the transformer block. Additionally, the model employs a task-specific input module of explicit visual prompting (EVP) during training that uses embedded features and high-frequency component (HFC) information as prompts. Road-SAM also utilizes a carefully designed frequency adapter fine-tuning mechanism, leveraging lightweight yet effective fine-tuning techniques to integrate domain-specific RS knowledge into the RE model, enhancing segmentation performance and making efficient use of computational resources. Comprehensive experiments on two sets of RE benchmark datasets demonstrate the effectiveness of the proposed method. Extensive ablation experiments further validate its superiority over multiple state-of-the-art (SOTA) RS RE algorithms, with updates applied to only 10% of the parameters.