Use of Generative Adversarial Network Algorithm in Super-Resolution Images to Increase the Quality of Digital Elevation Models Based on ALOS PALSAR Data

Abstract

Digital elevation models are responsible for providing altimetric information on a surface to be mapped. While global models of low and medium spatial resolution are available open source by several space agencies, the high- resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Here we address this limitation by the utilization of deep learning algorithms coupled with Single Image Super-Resolution techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based (Generative Adversarial Network-based) methodology enables the improvement of the initial spatial resolution of low-resolution images. In the geospatial data context, for example, these algorithms can be used with digital elevation models and satellite images. The methodological approach uses a dataset with digital elevation models SRTM (Shuttle Radar Topography Mission) (30 meters of spatial resolution) and ALOS PALSAR (12.5 meters of spatial  resolution), created with the objective of allowing the study to be carried  out, promoting the emergence of new research groups in the area as well as  enabling the comparison between the results obtained. It has been found that by increasing the number of iterations the performance of the  generated model was improved and the quality of the generated image increased. Furthermore, the visual analysis of the generated image against the high- and low-resolution ones showed a great similarity between the first two.