Automated Crater detection from Co-registered optical images, elevation maps and slope maps using deep learning

Abstract

Craters are topographic structures resulting from impactors striking the surface of planetary bodies. This paper proposes a novel way of simultaneously utilizing optical images, digital elevation maps (DEMs), and slope maps for automatic crater detection on the lunar surface. The proposed system utilizes Mask R–CNN by tuning it for the crater detection task. Two catalogs, namely, Head-LROC and Robbins, are used for performance evaluation, and extensive analysis of detection results for the lunar surface is performed for both of these catalogs. A recall value of 93.94% is obtained for the Head-LROC catalog, which has relatively strict crater markings. For the Robbins catalog, an exhaustive crater catalog based on relatively liberal marking, F 1 -score of the proposed system ranges from 64.27% to 81.33%, for different crater size ranges. The proposed system's generalization capability for crater detection on different terrains with different input data types is also evaluated. Experimental results show that the proposed system trained on the lunar surface can also detect craters on the Martian surface. This model is trained by simultaneously using lunar surface's optical images and DEMs with their corresponding slope maps; however, it is tested on an entirely different input data type, thermal IR images from the Martian surface. • Crater detection by simultaneously using optical images, DEMs, and slope data. • Evaluation of crater detection and localization on conservative and liberal catalogs. • Proposed system either outperforms or matches the current state-of-the-art. • For the first time exploration of training deep learning system on Robbins catalog. • Generalization capability explored on Martian surface with different input data type.