TTSR: A Transformer-Based Topography Neural Network for Digital Elevation Model Super-Resolution

Abstract

Digital elevation models (DEMs) are crucial geographical data source whereas the resolution of commonly used DEM products is low and cannot meet requirement of some detailed geo-related applications. Deep learning-based methods have been demonstrated to be effective in super-resolution (SR) techniques, which reconstruct high-resolution (HR) images from low-resolution (LR) images. However, existing deep learning methods have not fully considered the multi-scale spatial heterogeneity and topographic knowledge of DEM data that differentiate them from traditional images. These inevitably lead to the localized smoothing of the reconstructed DEM and influence the reliability downstream geographical analysis. This study proposes a transformer-based topography neural network (TTSR) for DEM SR incorporating a local-global deformable block (LGDB) for capturing the multi-scale spatial heterogeneity and topographic knowledge, a spatio-channel coupled channel attention (SimAM) mechanism for reallocating channel weights and providing a supplement of the global spatial features, and an improved terrain loss function (iLoss) for mitigating noise across datasets. TTSR was validated using two publicly available real-world DEM datasets for recovering DEM from 30 m to 10 m. The root mean square error (RMSE) of the proposed method was reduced by approximately 6-30%, 4-16%, and 1-9% in elevation accuracy, slope accuracy, and aspect accuracy, respectively, compared to the best one of those state-of-the-art methods. This research provides new insights for improving the accuracy of the DEM SR, which will help generate global high-resolution terrain products to geographical studies in the future.