Arbitrary Scale Super-Resolution Assisted Lunar Crater Detection in Satellite Images

Craters are one of the most studied planetary features used for different scientific analyses, such as estimation of surface age and surface processes. Satellite images utilized for crater detection often have low resolution (LR) due to hardware constraints and transmission time. Super-resolution (SR) is a practical and cost-effective solution; however, most SR approaches work on fixed integer scale factors, i.e., a single model can generate images of a specific resolution. In practical applications, SR on multiple scales provides various levels of detail, but training for each scale is resource-intensive. Therefore, this paper proposes a system for crater detection assisted with an arbitrary scale super-resolution approach (i.e., a single model can be used for multiple scale factors) for the lunar surface. Our work is composed of two subsystems. The first sub-system employs an arbitrary scale SR approach to generate super-resolved images of multiple resolutions. Subsequently, the second sub-system passes super-resolved images of multiple resolutions to a deep learning-based crater detection framework for identifying craters on the lunar surface. Employed arbitrary scale SR approach is based on a combination of convolution and transformer modules. For the crater detection sub-system, we utilize the Mask-RCNN framework. Using SR images of multiple resolutions, the proposed system detects 13.47% more craters from the ground truth than the craters detected using only the LR images. Further, in complex crater settings, specifically in overlapping and degraded craters, 11.84% and 15.01% more craters are detected as compared to the crater detection networks using only the LR images. The proposed system also leads to better localization performance, 3.19% IoU increment compared to the LR images