Abstract
Lunar craters are very important for estimating the geological age of the Moon, studying the evolution of the Moon, and for landing site selection. Due to a lack of labeled samples, processing times due to high-resolution imagery, the small number of suitable detection models, and the influence of solar illumination, Crater Detection Algorithms (CDAs) based on Digital Orthophoto Maps (DOMs) have not yet been well-developed. In this paper, a large number of training data are labeled manually in the Highland and Maria regions, using the Chang’E-2 (CE-2) DOM; however, the labeled data cannot cover all kinds of crater types. To solve the problem of small crater detection, a new crater detection model (Crater R-CNN) is proposed, which can effectively extract the spatial and semantic information of craters from DOM data. As incomplete labeled samples are not conducive for model training, the Two-Teachers Self-training with Noise (TTSN) method is used to train the Crater R-CNN model, thus constructing a new model—called Crater R-CNN with TTSN—which can achieve state-of-the-art performance. To evaluate the accuracy of the model, three other detection models (Mask R-CNN, no-Mask R-CNN, and Crater R-CNN) based on semi-supervised deep learning were used to detect craters in the Highland and Maria regions. The results indicate that Crater R-CNN with TTSN achieved the highest precision (of 91.4% and 88.5%, respectively) in the Highland and Maria regions, even obtaining the highest recall and F1 score. Compared with Mask R-CNN, no-Mask R-CNN, and Crater R-CNN, Crater R-CNN with TTSN had strong robustness and better generalization ability for crater detection within 1 km in different terrains, making it possible to detect small craters with high accuracy when using DOM data.
Highlights
IntroductionCraters are the main type of lunar topography, which record information about past meteorite impacts and solar activities, such as solar winds and cosmic X-ray radiation [1]
Craters are the main type of lunar topography, which record information about past meteorite impacts and solar activities, such as solar winds and cosmic X-ray radiation [1].craters are used to study the geological age [2,3], evolution, dynamic mechanisms, and the meteorite impact history [4,5] of the Moon
We describe a robust and highly accurate method based on Crater R-CNN and
Summary
Craters are the main type of lunar topography, which record information about past meteorite impacts and solar activities, such as solar winds and cosmic X-ray radiation [1]. Craters are used to study the geological age [2,3], evolution, dynamic mechanisms, and the meteorite impact history [4,5] of the Moon. Craters are a hindrance to lunar landings and cruising, affecting landing site selection, rover navigation and positioning, and cruising route planning [6]. As craters play an important role in lunar scientific research and engineering, lunar crater detection has become a critical problem. Several crater databases have been built using low-resolution remote sensing data.
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