Terrain Self-Similarity-Based Transformer for Generating Super Resolution DEMs

High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques currently low-cost feasible methods used obtaining high-resolution DEMs. Single-image (SISR) have become popular in DEM SR recent years. However, has not yet utilized reference-based image (RefSR) techniques. In this paper, we propose a terrain self-similarity-based transformer (SSTrans) to generate It is method that automatically acquires reference images using self-similarity. To verify the proposed model, conducted experiments on four distinct types of compared them results from bicubic, SRGAN, SRCNN approaches. The experimental show SSTrans performs well all terrains outstanding advantages complex uneven surface terrains.