In the UAV visual navigation and positioning system, the topological relationship between road intersections can provide an important basis for remote sensing image matching and localization. Therefore, this study proposes a road intersection matching method based on triangular structure, which constructs a semantic expression library of road intersection targets as a benchmark database for road intersection matching; and then formulates a matching strategy based on the triangular similarity principle with the salient features between road intersections to complete the accurate and efficient localization of road intersections. The simulation experiment results show that this algorithm is stable and robust, and is not affected by brightness, noise and view angle. Under the 6Km*6Km large scene matching condition, it takes 7.8S from intersection detection to intersection geolocation localization, correctly matches 8 road intersections, and meets the UAV localization requirements.
As urbanization and the rapid development of remote sensing technology, the interpretation of urban roads and intersections using high-resolution remote sensing images has become the focus of studying urban planning and road network structure. However, the current research is only limited to extracting road intersections, and few studies have constructed their topological relationships and analyzed the spatial distribution of road networks. Therefore, in this study, the intersection targets are extracted based on YOLOv5s model, and then the topological relationship of intersections is constructed by Delaunay Triangulation. The accuracy of the optimal model is 0.9415 for mAP, 0.8985 for F1, 0.8611 for Recall, and 0.9394 for Precision. By analyzing their characteristics such as area, side length, PESL, AESL, ESLR and interior angle, it can be found that the density of road intersections in the study area is high, the spacing of adjacent intersections of most roads is relatively similar, and most of the roads intersect vertically. This study first extracts road intersections from remote sensing images and then analyzes their topological relationships to provide research ideas for large-scale urban road network analysis and multi-source road data matching.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.