Data Processing Technology of UAV Tilt Photogrammetry Based on Multi-source Geographic Information

With the rapid development of UAV technology, UAV tilt photogrammetry has become one of the important means to obtain geographic information data. However, due to the challenges of diverse geographic information data sources and inconsistent data quality, how to effectively integrate multi-source geographic information data has become a key issue. Aiming at this problem, this study proposes an image fusion method based on deep learning, aiming at improving the fusion quality and processing efficiency of geographic information data. Firstly, the basic principle and data acquisition process of UAV tilt photogrammetry are introduced, including aircraft selection, route planning, selection of photographic equipment and sensors. Then, the methods and technologies of multi-source geographic information fusion are discussed, including data preprocessing, feature extraction, data registration and image fusion. Then, the specific implementation steps and algorithms of the image fusion method based on deep learning adopted in this paper are expounded. Through the verification on experimental data sets, the advantages of the proposed method are demonstrated, including fusion effect, model performance and processing efficiency. Finally, the research results are summarized and analyzed, and the significance and application prospect of this method in the fusion of multi-source geographic information are pointed out.