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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| Vydáno v: | Icarus (New York, N.Y. 1962) Ročník 410; s. 115909 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.03.2024
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| ISSN: | 0019-1035, 1090-2643 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 115909 |
| Author | Zou, Chen Cui, Feifei Lai, Jialong Liu, Yanshuang Xu, Yi Qiao, Le |
| Author_xml | – sequence: 1 givenname: Chen surname: Zou fullname: Zou, Chen organization: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China – sequence: 2 givenname: Jialong surname: Lai fullname: Lai, Jialong email: laijialong@jxust.edu.cn organization: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China – sequence: 3 givenname: Yanshuang surname: Liu fullname: Liu, Yanshuang organization: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China – sequence: 4 givenname: Feifei surname: Cui fullname: Cui, Feifei organization: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China – sequence: 5 givenname: Yi surname: Xu fullname: Xu, Yi organization: State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau – sequence: 6 givenname: Le surname: Qiao fullname: Qiao, Le organization: Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Physics, Institute of Space Sciences, Shandong University, Weihai, Shandong 264209, China |
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| Keywords | Deep learning Small craters detection Lunar crater dating Algorithm optimization |
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| Title | Small lunar crater identification and age estimation in Chang'e-5 landing area based on improved Faster R-CNN |
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