Abstract The digital elevation model (DEM) is an important basic data tool applied in geoscience applications. Because of its high cost and long development cycle of enhancing hardware performance, designing the related models and algorithms to improve the resolution of DEM is of considerable significance. At present, there is little research on DEM super-resolution based on deep learning, and the results of the reconstructed DEMs obtained by existing methods are inaccurate. Therefore, deepening of the network layers is utilized to improve the accuracy of a reconstructed DEM. This paper designs a neutral network model with 30 convolutional layers to learn the feature mapping relationship between a low- and high-resolution DEM. To avoid the problem of network degradation caused by increasing the number of convolutional layers, residual learning is introduced to accelerate the convergence speed of the model, thereby preferably realizing the DEM super-resolution process. The results show that DEM super-resolution based on a deep residual network is better than that obtained using a neural network with fewer convolutional layers, and the reconstructed result of the DEM based on a deep residual network is remarkably improved in terms of the peak signal to noise ratio and visual effect.

中文翻译：

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基于深度残差网络的数字高程模型超分辨率重建

摘要 数字高程模型（DEM）是地球科学应用中重要的基础数据工具。由于其提高硬件性能的成本高、开发周期长，设计相关模型和算法以提高DEM的分辨率具有相当重要的意义。目前基于深度学习的DEM超分辨率研究较少，现有方法重建的DEM结果不准确。因此，利用网络层的加深来提高重构 DEM 的精度。本文设计了一个具有 30 个卷积层的中性网络模型来学习低分辨率和高分辨率 DEM 之间的特征映射关系。为避免增加卷积层数导致网络退化的问题，引入残差学习来加快模型的收敛速度，从而更好地实现DEM超分辨率过程。结果表明，基于深度残差网络的DEM超分辨率优于使用较少卷积层的神经网络获得的超分辨率，基于深度残差网络的DEM重构结果在峰值信号方面有显着提升噪声比和视觉效果。