Super-resolution of Digital Elevation Models With Deep Learning Solutions
Kubade Ashish Ashokrao
Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, hazard prevention and mitigation planning, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high-resolution details in the terrain. However, currently available public datasets, providing terrain scans in the form of Digital Elevation Models (DEMs) have low resolution compared with the terrain information available in other modalities like aerial images. Publicly available DEM datasets for most parts of the world have a resolution of 30 m whereas the aerial images or satellite images are available at a resolution of 50 cm. The cost involved in capturing of such high-resolution DEMs (HRDEMs) turns out to be a major hurdle for making such high-resolution available in the public domain. This motivates us to provide a software solution for generating high-resolution DEM from the existing low-resolution DEMs (LRDEMs). In natural image domain, super-resolution has set up higher benchmarks by incorporating deep learning based solutions. Despite such tremendous success in image super-resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. A few of them used additional modalities as aerial images or satellite images, temporal sequence of DEMs etc., to generate high-resolution terrains. However, the applicability of these methods is highly subject to the available input formats. In this research effort, we explore a new direction in DEM super-resolution by using feedback neural networks. Availing the capability of feedback neural networks to redefine the features learned by shallow layers of the network, we design DSRFB, a DEM super-resolution architecture that generates high-resolution DEM with a super-resolution factor of 8X with minimal input. Our experiments on Pyrenees and Tyrol mountain range datasets show that DSRFB can perform near to the state-of-the-art without using information from any additional modalities like aerial images. Further, by understanding the limitations of DSRFB, which primarily occur in case of highly degraded low-resolution input. In such cases, the major structures are entirely lost and the reconstruction becomes challenging. In such cases, to avail the elevation cues from alternate sources of information becomes necessary. To utilize such information from other modalities, we inherit the attention mechanism from natural language processing (NLP) domain. We integrate the attention mechanism into the feedback network to present Attentional Feedback Module (AFM). Our proposed network, Attentional Feedback vivii Network (AFN) with AFM as a backbone, outperforms the state-of-the-art methods with the best margin of 7.2%. We also emphasize on the reconstruction of the structures across patch boundaries. While generating HRDEM by splitting large DEM tiles into patches, we propose to use overlapped tiles and generate an aggregated response to dilute the artefacts due to structural discontinuities. To summarize, in this research, we propose two methods DSRFB and AFN to generate a high- resolution DEM from existing low-resolution DEM. While DSRFB achieves near to the state-of-the-art performance, coupling DSRFB with attentional mechanism (i.e., AFN) outperforms state-of-the-art methods.
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||Avinash Sharma,K S Rajan