A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning

作者：Xu Lin, Qingqing Zhang, Hongyue Wang, Chaolong Yao, Changxin Chen, Lin Cheng, Zhaoxiong Li · 发表于：Remote Sensing · 年份：2022 · DOI：10.3390/rs14092181 · 被引用次数：27 · 研究领域：Advanced Image Processing Techniques、Advanced Vision and Imaging、Optical measurement and interference techniques

The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental ...