Automated Extraction of Crater Rims on 3D Meshes Combining Artificial Neural Network and Discrete Curvature Labeling

One of the challenges of planetary science is the age determination of geological units on the surface of the different planetary bodies in the solar system. This serves to establish a chronology of the geological events occurring on these different bodies, hence to understand their formation and evolution processes. An approach for dating planetary surfaces relies on the analysis of the impact crater densities with size. Approaches have been proposed to automatically detect impact craters in order to facilitate the dating process. They rely on color values from images or elevation values from Digital Elevation Models (DEM). In this article, we propose a new approach for crater detection, more specifically using their rims. The craters can be characterized by a round shape that can be used as a feature. The developed method is based on an analysis of the DEM geometry, represented as a 3D mesh, followed by curvature analysis. The classification process is done with one layer perceptron. The validation of the method is performed on DEMs of Mars, acquired by a laser altimeter aboard NASA’s Mars Global Surveyor spacecraft and combined with a database of manually identified craters. The results show that the proposed approach significantly reduces the number of false negatives compared to others based on topographic information only.