Enhancing SRTM digital elevation models with deep-learning-based super-resolution image generation

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 SISR techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based methodology enables the improvement of the initial spatial resolution of low-resolution images. A dataset with different pairs of digital elevation models was 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.

Keywords:
Digital Elevation Model; Generative Adversarial Network; Image Super Resolution; Machine Learning; Deep Learning; Neural Networks; Cartographic Production