EGU22-486
https://doi.org/10.5194/egusphere-egu22-486
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Enhancing planetary imagery with the holistic attention network algorithm

Denis Maxheimer1, Ioannis Markonis1, Masner Jan2, Curin Vojtech1, Pavlik Jan2, and Solomonidou Anezina3
Denis Maxheimer et al.
  • 1Czech University of Life Sciences, Faculty of Environmental Sciences, Czechia (xgold007@studenti.czu.cz)
  • 2Czech University of Life Sciences, Faculty of Economics and Management, Czechia
  • 3California Institute of Technology: Pasadena, CA, US

The recent developments in computer vision research in the field of Single Image Super Resolution (SISR)

can help improve the satellite imagery data quality and, thus, find application in planetary exploration.

The aim of this study is to enhance planetary surface imagery, in planetary bodies that there are

available data but in a low resolution. Here, we have applied the holistic attention network (HAN)

algorithm to a set of images of Saturn’s moon Titan from the Titan Radar Mapper instrument in its

Synthetic Aperture Radar (SAR) mode, which was on board the Cassini spacecraft. HAN can find

correlations among hierarchical layers, channels of each layer, and all positions of each channel, which

can be interpreted as an application and intersection of previously known models. The algorithm used

in our case-study was trained on 5000 grayscale images from HydroSHED Earth surface imagery dataset

resampled over different resolutions. Our experimental setup was to generate High Resolution (HR)

imagery from eight times lower resolution (x8 scale). We followed the standard workflow for this

purpose, which is to first train the network enhancing x2 scale to HR, then x4 scale to x2 scale, and

finally x8 scale to x4 scale, using subsequently the results of the previous training. The promising results

open a path for further applications of the trained model to improve the imagery data quality, and aid

in the detection and analysis of planetary surface features.

How to cite: Maxheimer, D., Markonis, I., Jan, M., Vojtech, C., Jan, P., and Anezina, S.: Enhancing planetary imagery with the holistic attention network algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-486, https://doi.org/10.5194/egusphere-egu22-486, 2022.

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