An Enhanced Residual Feature Fusion Network Integrated with a Terrain Weight Module for Digital Elevation Model Super-Resolution

Abstract

The scale of digital elevation models (DEMs) is vital for terrain analysis, surface simulation, and other geographic applications. Compared to traditional super-resolution (SR) methods, deep convolutional neural networks (CNNs) have shown great success in DEM SR. However, in terms of these CNN-based SR methods, the features extracted by the stackable residual modules cannot be fully utilized as the depth of the network increases. Therefore, our study proposes an enhanced residual feature fusion network (ERFFN) for DEM SR. The designed residual fusion module groups four residual modules to make better use of the local residual features. Meanwhile, the residual structure is refined by inserting a lightweight enhanced spatial residual attention module into each basic residual block to further strengthen the efficiency of the network. Considering the continuity of terrain features, terrain weight modules are integrated into the loss module. Based on two large-scale datasets, our ERFFN shows a 10–20% reduction in the mean absolute error and the lowest error in terrain features, such as slope, demonstrating the superiority of an ERFFN-based DEM SR over state-of-the-art methods. Finally, to demonstrate potential value in real-world applications, we deploy the ERFFN to reconstruct a large geographic area covering 44,000 km2 which contains missing parts.

Keywords

• digital elevation models
• terrain features
• fusion of residual features
• super-resolution