Automatic detection for small-scale lunar impact crater using deep learning

The impact crater is the most dominant geomorphic structural units over the planetary surface and its accurate extraction is of great significance to investigate the evolution of the Moon, the impact history of the solar system and the space environment. Through the impact crater's identification, especially small-scale, and their spatial distribution characteristics, the impact flux, morphological characteristics, age of impact craters and surface degradation can be revealed. Therefore, this paper presents an automatic method for the impact crater detection, especially small-scale ones, using anchor -free deep learning. Due to the limitation of traditional object proposals for detecting different types of impact craters, we retrain the anchor -free CenterNet model using a transfer learning strategy, where the detection task of impact crater is formulated into the detection of its center point and regression of its property (i.e., size) without nonmaximal suppression. We select the stacked Hourglass network as backbone to aggregate different levels of feature for enhancing the capability of estimating the impact crater centers. Moreover, we find the center points of impact craters on the image features' heatmap only based on their locations, instead of box overlap, which allows us to detect different types of impact craters, even the impact craters that contain other impact craters. The model is trained in an end -to -end manner and applied to detect the impact craters on the lunar images between +/- 50 degrees of latitude, with the spatial resolution of 100 m/pixel, from Wide Angle Camera (WAC) onboard Lunar Reconnaissance Orbiter (LRO) mission. The obtained results are compared relative to an existing crater database both qualitatively and quantitatively that suggests the reliability and robustness of the developed method in the automatic detection of small-scale impact craters, where the smallest one is 500 m. Moreover, the developed method is capable to detect different types of impact craters, including dispersal and connective ones, with the recall of 73.66% and precision of 78.27% compared with an existing crater database. The code is publicly available at https://github.com/ShuoweiZhang/crater_detection. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.