Simultaneous Super-resolution and Depth Estimation for Satellite Images Based on Diffusion Model

Satellite images provide an effective way to observe the earth surface on a large scale. 3D landscape models can provide critical structural information, such as forestry and crop growth. However, there has been very limited research to estimate the depth and the 3D models of the earth based on satellite images. LiDAR measurements on satellites are usually quite sparse. RGB images have higher resolution than LiDAR, but there has been little research on 3D surface measurements based on satellite RGB images. In comparison with in-situ sensing, satellite RGB images are usually low resolution. In this research, we explore the method that can enhance the satellite image resolution to generate super-resolution images and then conduct depth estimation and 3D reconstruction based on higher-resolution satellite images. Leveraging the strong generation capability of diffusion models, we developed a simultaneous diffusion model learning framework that can train diffusion models for both super-resolution images and depth estimation. With the super-resolution images and the corresponding depth maps, 3D surface reconstruction models with detailed landscape information can be generated. We evaluated the proposed methodology on multiple satellite datasets for both super-resolution and depth estimation tasks, which have demonstrated the effectiveness of our methodology.