方向引导与拓扑感知的光学遥感道路提取网络
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关键词：道路提取；连通关系；方向引导；全粒度特征融合；拓扑感知中图分类号：TP394.1;TH691.9 文献标识码：Adoi：10.37188/OPE.20253310.1638 CSTR：32169.14.OPE.20253310.1638
Optical remote sensing road extraction network with directional guidance and topological awareness
MENG Yuebo 1，2* ，HUANG Xinyu1，²， SU Shilong 1，2 ，WANG Heng1，2 （1. College of Information and Control Engineering，Xi'an University of Architecture and Technology，Xi'an 710055，China; 2.Key Laboratory of Construction Robots for Higher Education in Shaanxi Province， Xi'an710055，China） * Corresponding author，E-mail： mengyuebo@l63.com
Abstract：To address the challenges of weak connectivity，subtle branch omission，and topological inconsistency between predicted and real road networks in optical remote sensing image road extraction，this paper proposed a road extraction network with directional guidance and topological awareness. First，the multi-path directional guidance module was designed to model multi-directional connectivity relationships. By decoupling and independently learning connectivity features across distinct directions，this module enhanced inter-branch linkages and improved segmentation continuity. Second，the ful granular complementary feature guidance module integrated fine-grained and coarse-grained features，reinforcing both road details and semantic representations to strengthen the network's capability in capturing subtle branches. Fi- nally，a topological awareness function was introduced to quantify geometric structural discrepancies from a topological perspective，thereby constraining the topological consistency between predicted and real road networks. The proposed model achieves Fl scores of 81. 95% and 79.98% on the DeepGlobe and Massachusetts datasets，outperforming the state-of-the-art methods by 0.73% and 1.5% ，respectively.The IoU metrics reach 69.35% and 66.38% ，with improvements of 0.98% and 0.66% over existing benchmarks.Experimental results demonstrate that RDTA-Net significantly surpasses mainstream methods in both road extraction accuracy and completeness. Furthermore，it exhibits robust performance in complex scenarios involving occlusions，noise，and illumination variations.
Key words： road extraction；connectivity；directional guidance； full granular feature fusion； topology wareness
1引言
随着遥感技术日益发展，光学遥感影像数据的获取能力与成像质量不断提升，在海量的数据支持下，道路提取技术得到蓬勃发展，该技术利用遥感影像提取出道路网络信息，在自动驾驶、环境监测、灾后救援1和路网规划等领域具有重要意义，为城市智能交通和地理信息系统建设提供可靠的数据基础。（剩余23378字）
