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

作者：Xin Zheng, Zelun Bao, Qian Yin · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15071954 · 被引用次数：20 · 研究领域：Advanced Image Processing Techniques、Advanced Vision and Imaging、Landslides and related hazards

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