Small lunar crater identification and age estimation in Chang'e-5 landing area based on improved Faster R-CNN

The Chang'e-5 (CE-5) mission marks China's first lunar sample return endeavor, with its landing site (43.06°N, 51.92°W) situated in the Mons Rümker region of the northern Oceanus Procellarum on the Moon. This region hosts some of the youngest mare basalts of the Moon and contains a relativ...

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Bibliographic Details
Published in:Icarus (New York, N.Y. 1962) Vol. 410; p. 115909
Main Authors: Zou, Chen, Lai, Jialong, Liu, Yanshuang, Cui, Feifei, Xu, Yi, Qiao, Le
Format: Journal Article
Language:English
Published: Elsevier Inc 01.03.2024
Subjects:
Algorithm optimization
Deep learning
Lunar crater dating
Small craters detection
Deep learning
Small craters detection
Lunar crater dating
Algorithm optimization
ISSN:0019-1035, 1090-2643
Online Access:Get full text
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Summary:The Chang'e-5 (CE-5) mission marks China's first lunar sample return endeavor, with its landing site (43.06°N, 51.92°W) situated in the Mons Rümker region of the northern Oceanus Procellarum on the Moon. This region hosts some of the youngest mare basalts of the Moon and contains a relatively youthful geologic unit characterized by crater's equilibrium diameters slightly over 100 m. By refining the Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm and leveraging high-resolution imagery to create training samples, accurate identification of lunar craters can be achieved. In this study, we enhance the algorithm in aspects such as anchor boxes and Region of Interest alignment. Additionally, we have utilized high-resolution images for training, and identify and statistics craters within the CE-5 landing area. Ultimately, our model attains a validation set Recall of 90%, Precision of 69%, and an Average Precision score of 0.83. Notably, in certain scales, such as for the crater larger than 400 m, recognition results reach Precision of 88% and Recall of 89%. The findings of this study are mapped into a crater catalog. Furthermore, we predict crater density and integrate it with geochronological functions to estimate the absolute model age of nine major geologic units within the CE-5 landing area. The results are generally in agreement with those of other studies who have used manual methods for crater counting, and verify the correctness of our automatic crater identification results. •Enhanced small crater recognition in deep learning: sub-kilometer scale precision.•Employed three strategies to improve Faster RCNN in identifying lunar craters.•Created a crater catalog and estimated model age for the Chang'E-5 area.
ISSN:0019-1035
1090-2643
DOI:10.1016/j.icarus.2023.115909