DEM super-resolution guided by shaded relief using attention-based fusion

Abstract

Deep-learning based approaches have been proven effective for Digital Elevation Model (DEM) super-resolution (SR) tasks. Previous networks typically treat DEM elevation values as single-channel image for input. However, DEM images alone cannot fully capture spatial and terrain features. Shaded relief images (SRIs), derived from DEMs, serve as crucial visual cues that intuitively convey terrain characteristics, addressing the limitations of DEM images and providing synergistic benefits for training DL models. The primary challenge in utilizing SRIs for guiding DEM SR lies in accurately selecting a consistent structure to extract and effectively integrate features from SRIs and DEMs. In this study, we propose an Attention-based Hierarchical Terrain Fusion (AHTF) framework for guided DEM SR. Specifically, an Attention-based Feature Fusion Module (AFFM) is designed to efficiently fuse relevant information from LR DEM and SRI, which includes a feature enhancement block to select valuable features and a feature recalibration block to fuse diverse terrain features. Additionally, we optimize the loss function from the perspectives of terrain analysis and visual effects. We validate AHTF on our newly constructed real-world Shade-DEM SR dataset and two open-source DEM SR datasets. Compared to the current state-of-the-art methods, our AHTF achieves the best results in terms of root mean square error (RMSE) for elevation, slope, and aspect. Furthermore, the extracted stream networks are closer to real-world conditions. This study offers new insights and methods for further research and application in the field of DEM super-resolution. Our dataset can be obtained at https://doi.org/10.6084/m9.figshare.25590945.