Automatic Crater Shape Retrieval using Unsupervised and Semi-Supervised Systems

Impact craters are formed due to continuous impacts on the surface of planetary bodies. Most recent deep learning-based crater detection methods treat craters as circular shapes, and less attention is paid to extracting the exact shapes of craters. Extracting precise shapes of the craters can be helpful for many advanced analyses, such as crater formation. This paper proposes a combination of unsupervised non-deep learning and semi-supervised deep learning approach to accurately extract shapes of the craters and detect missing craters from the existing catalog. In unsupervised non-deep learning, we have proposed an adaptive rim extraction algorithm to extract craters' shapes. In this adaptive rim extraction algorithm, we utilized the elevation profiles of DEMs and applied morphological operation on DEM-derived slopes to extract craters' shapes. The extracted shapes of the craters are used in semi-supervised deep learning to get the locations, size, and refined shapes. Further, the extracted shapes of the craters are utilized to improve the estimate of the craters' diameter, depth, and other morphological factors. The craters' shape, estimated diameter, and depth with other morphological factors will be publicly available.

Automated Crater Detection from Co-registered Optical Images, Elevation Maps and Slope Maps using Deep Learning

Impact craters are formed as a result of continuous impacts on 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. Mask R-CNN, tuned for the crater detection task, is utilized in this paper. Two catalogs, namely, Head-LROC and Robbins, are used for the performance evaluation. Exhaustive analysis of the detection results on the lunar surface has been performed with respect to both Head-LROC and Robbins catalog. With the Head-LROC catalog, which has relatively strict crater markings and larger possibility of missing craters, recall value of 94.28\% has been obtained as compared to 88.03\% for the baseline method. However, with respect to a manually marked exhaustive crater catalog based on relatively liberal marking, significant precision and recall values are obtained for different crater size ranges. The generalization capability of the proposed method in terms of crater detection on a different terrain with different input data type is also evaluated. We show that the proposed model trained on the lunar surface with optical images, DEMs and corresponding slope maps can be used to detect craters on the Martian surface even with entirely different input data type, such as thermal IR images from the Martian surface.