Crater Detection, Classification and Contextual Information Extraction in Lunar Images Using Profile-based Algorithm

Introduction: Impact craters are the dominant feature on any planetary surface and this dominance is used for the estimation of age of planets by crater count [1]. Such dominance gathered attention to detect them automatically in DTM [2] and panchromatic [3] images. Most of the crater detection algorithm (CDA) fall short to classify the crater and extract contextual information (presence/absence of ejecta) from it. The algorithm proposed in this paper aims to automatically detect, and extract contextual information from the simple lunar craters from the Selen Terrain Camera images. Apart from detection, the ability of our algorithm is to classify the simple lunar craters into roundand flat-floor type, indicate the presence of ejecta and associate it with the corresponding crater. This algorithm was designed to detect craters of considerable size and diameter to avoid small craters from where no morphological information can be obtained.