Generative DEM Void Filling With Terrain Feature-Guided Transfer Learning Assisted by Remote Sensing Images

The quality of digital elevation models (DEMs) is easily affected by data voids in regions with complex terrain conditions. Numerous methods have been proposed to fill DEM voids by effectively exploiting the topographic information from neighboring areas or auxiliary DEMs. However, few studies have considered the integration of multi-modal data, which can provide valuable supplementary information in the areas with no high-quality reference DEM data. In this letter, we propose a generative DEM void filling method by exploring the integration of optical remote sensing images. The core idea is to utilize the image textures to infer the elevation values in the void regions with terrain texture-guided transfer learning. Specifically, the image context attention module (ICAM) is used to preliminarily estimate the missing topographic features by searching the similar patches with the guidance of image context. The terrain feature-guided residual pixel attention block (TFG-RPAB) is then employed to refine the void-filled features by transferring the image textures to topographic features. Finally, the void-filled DEM can be obtained by decoding the reconstructed topographic features. The results shows that the RMSE of RSAGAN is improved by 14.5% to 71.5% when DEM void filling. Both quantitative and qualitative evaluations demonstrate the superiority of the proposed method over the competitive methods in terms of DEM void filling. The source code is available at https://github.com/gaobingcug/RSAGAN.