Abstract
High-resolution digital elevation models (DEMs) are widely used in many fields such as mapping, hydrology, meteorology and geology, where they can improve the accuracy and reliability of many geographic analysis applications as an input. However, due to the high cost and difficulty of acquiring high-resolution DEMs, as well as the problems of edge smoothing, data distortion and fractures in reconstructed ground surfaces with traditional super-resolution DEM reconstruction techniques. Inspired by the excellence of generative adversarial neural networks in super-resolution image analysis, this paper investigates an approach for DEM super-resolution reconstruction with deep residual generative adversarial network. An advanced DEM Super-resolution Generative Adversarial Network (D-SRCAGAN) is proposed in this paper, which can reconstruct a quadruple higher resolution DEM by using low-resolution DEM. Compared with the bicubic and SRGAN methods, the D-SRCAGAN method reconstruction results can retain more topographic features and obtain higher RMSE values.
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