Abstract:

Remote sensing images can provide rich information on road networks, and traditional algorithms can extract details about visible roads from images based on local features. However, such algorithms often encounter notable issues with robustness due to their lack of global semantic information as guidance. To address this issue, we propose an algorithm to extract road areas from remote sensing images based on spatial semantic guidance. The proposed approach combines traditional local information with deep semantic features to learn to initialize a network of roads based on semantic segmentation networks. On this basis, we designed a centerline extraction module (CEM) to extract the centerlines of visible roads as guided by a global semantic model and descriptor. Finally, we obtain a diagram of the structure of the road topology by combining the obtained centerlines with the semantic features using a topology construction algorithm. This ensures the preservation of detailed features by incorporating local details. We evaluated the time performance of the proposed algorithm and its accuracy in extracting roads using publicly available remote sensing image data, and the results confirm that the algorithm performed well and was sufficiently effective to achieve further 3D reconstruction. Visualizations of these findings are also provided.

Key words: remote sensing images, image semantics, detailed features, global features, road topology