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Orthographic Video Map Generation Considering 3D GIS View Matching

I tiakina i:
I whakaputaina i:ISPRS International Journal of Geo-Information vol. 14, no. 10 (2025), p. 398-420
Kaituhi matua: Zhang, Xingguo
Ētahi atu kaituhi: Meng Xiangfei, Zhang, Li, Ling Xianguo, Sen, Yang
I whakaputaina:
MDPI AG
Ngā marau:
Image resolution
Algorithms
Video recordings
Calibration
Cameras
Natural resources
Remote sensing
Mapping
Mountainous areas
Maps
Machine learning
Mountain regions
Deep learning
Orthography
Geometric accuracy
Matching
Frames (data processing)
Accuracy
Neural networks
Towers
Three dimensional models
Smart cities
Video data
Methods
Geographic information systems
Rescue operations
Surveillance
Geographical information systems
Real time
Parameters
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Whakarāpopotonga:Converting tower-mounted videos from perspective to orthographic view is beneficial for their integration with maps and remote sensing images and can provide a clearer and more real-time data source for earth observation. This paper addresses the issue of low geometric accuracy in orthographic video generation by proposing a method that incorporates 3D GIS view matching. Firstly, a geometric alignment model between video frames and 3D GIS views is established through camera parameter mapping. Then, feature point detection and matching algorithms are employed to associate image coordinates with corresponding 3D spatial coordinates. Finally, an orthographic video map is generated based on the color point cloud. The results show that (1) for tower-based video, a 3D GIS constructed from publicly available DEMs and high-resolution remote sensing imagery can meet the spatialization needs of large-scale tower-mounted video data. (2) The feature point matching algorithm based on deep learning effectively achieves accurate matching between video frames and 3D GIS views. (3) Compared with the traditional method, such as the camera parameters method, the orthographic video map generated by this method has advantages in terms of geometric mapping accuracy and visualization effect. In the mountainous area, the RMSE of the control points is reduced from 137.70 m to 7.72 m. In the flat area, it is reduced from 13.52 m to 8.10 m. The proposed method can provide a near-real-time orthographic video map for smart cities, natural resource monitoring, emergency rescue, and other fields.
ISSN:2220-9964
DOI:10.3390/ijgi14100398
Puna:Advanced Technologies & Aerospace Database