Enhancing planetary imagery with the holistic attention network algorithm

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 areavailable 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 itsSynthetic Aperture Radar (SAR) mode, which was on board the Cassini spacecraft. HAN can findcorrelations among hierarchical layers, channels of each layer, and all positions of each channel, whichcan be interpreted as an application and intersection of previously known models. The algorithm usedin our case-study was trained on 5000 grayscale images from HydroSHED Earth surface imagery datasetresampled 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 thispurpose, which is to first train the network enhancing x2 scale to HR, then x4 scale to x2 scale, andfinally x8 scale to x4 scale, using subsequently the results of the previous training. The promising resultsopen a path for further applications of the trained model to improve the imagery data quality, and aidin the detection and analysis of planetary surface features.