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A Machine Learning Approach to Crater Classification from Topographic Data TEXT SIZE: A A A
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.
 

Publication name

 REMOTE SENSING Volume: 11 Issue: 21 Article Number: 2594 DOI: 10.3390/rs11212594 Published: NOV 2019

Author(s)

 Liu, Qiangyi; Cheng, Weiming; Yan, Guangjian; Zhao, Yunliang; Liu, Jianzhong

Corresponding author(s) 

 CHEN WEiming
 chengwm@lreis.ac.cn  
 Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 
 Univ Chinese Acad Sci, Beijing 100049, Peoples R China
 Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
 CAS Ctr Excellence Comparat Planetol, Hefei 230052, Anhui, Peoples R China

Author(s) from IGCAS   LIU Jianzhong

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