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

Abstract

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 results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method.