Research on the Fusion Algorithm of Drone Images and Satellite Imagery

With the rapid development of drone technology, drone imagery has become an important means of obtaining high-resolution surface information. However, due to the operational height and range limitations of drones, there are issues of limited coverage and small data volume in drone imagery. Meanwhile, satellite imagery offers extensive coverage and a large amount of data but with lower resolution. In order to fully utilize the advantages of drone imagery and satellite imagery, and improve the accuracy of surface information extraction and spatial resolution, researchers have conducted studies on the fusion algorithms of drone imagery and satellite imagery. This article provides a review and analysis of the fusion algorithms for drone imagery and satellite imagery. Firstly, the characteristics and advantages of drone imagery and satellite imagery are introduced, emphasizing the importance of integrating the two. Furthermore, data loading and preprocessing techniques are discussed. Then, common fusion methods for drone imagery and satellite imagery are detailed, including pixel-level fusion, feature-level fusion, and decision-level fusion, among others. The evaluation methods for fusion quality are also explained. Finally, research achievements from both domestic and international sources are presented.