Terrain feature-aware deep learning network for digital elevation model superresolution

Abstract

Neural networks (NNs) have demonstrated the potential to recover finer textural details from lower-resolution images by superresolution (SR). Given similar grid-based data structures, some researchers have transferred image SR methods to digital elevation models (DEMs). These efforts have yielded better results than traditional spatial interpolation methods. However, terrain data present inherently different characteristics and practical meanings compared with natural images. This makes it unsuitable for existing SR methods on perceptually visual features of images to be directly adopted for extracting terrain features. In this paper, we argue that the problem lies in the lack of explicit terrain feature modeling and thus propose a terrain feature-aware superresolution model (TfaSR) to guide DEM SR towards the extraction and optimization of terrain features. Specifically, a deep residual module and a deformable convolution module are integrated to extract deep and adaptive terrain features, respectively. In addition, explicit terrain feature-aware optimization is proposed to focus on local terrain feature refinement during training. Extensive experiments show that TfaSR achieves state-of-the-art performance in terrain feature preservation during DEM SR. Specifically, compared with the traditional bicubic interpolation method and existing neural network methods (SRGAN, SRResNet, and SRCNN), the RMSE of our results is improved by 1.1% to 23.8% when recovering the DEM from 120 m to 30 m, by 4.9% to 22.7% when recovering the DEM from 60 m to 30 m, and by 7.8% to 53.7% when recovering the DEM from 30 m to 10 m. The source code that has been developed is shared on Figshare (https://doi.org/10.6084/m9.figshare.19597201).