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Open AccessArticle

A Machine Learning Approach to Crater Classification from Topographic Data

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State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
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Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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CAS Center for Excellence in Comparative Planetology, Hefei 230052, China
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State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100088, China
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Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China
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Lunar and Planetary Science Research Center, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2594; https://doi.org/10.3390/rs11212594
Received: 26 September 2019 / Revised: 28 October 2019 / Accepted: 4 November 2019 / Published: 5 November 2019
(This article belongs to the Special Issue Lunar Remote Sensing and Applications)
Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance. View Full-Text
Keywords: moon; distinguish primary craters from secondary craters; machine learning; crater characteristics moon; distinguish primary craters from secondary craters; machine learning; crater characteristics
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MDPI and ACS Style

Liu, Q.; Cheng, W.; Yan, G.; Zhao, Y.; Liu, J. A Machine Learning Approach to Crater Classification from Topographic Data. Remote Sens. 2019, 11, 2594.

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