中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
A Machine Learning Approach to Crater Classification from Topographic Data

文献类型：期刊论文

作者Liu, Qiangyi7,8; Cheng, Weiming1,6,7,8; Yan, Guangjian2,3; Zhao, Yunliang4; Liu, Jianzhong5,7
刊名REMOTE SENSING
出版日期2019-11-01
卷号11期号:21页码:30
关键词moon distinguish primary craters from secondary craters machine learning crater characteristics
DOI10.3390/rs11212594
通讯作者Cheng, Weiming(chengwm@lreis.ac.cn)
英文摘要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.
WOS关键词SIZE-FREQUENCY DISTRIBUTION ; ORBITER LASER ALTIMETER ; SECONDARY CRATERS ; IMPACT CRATERS ; MORPHOLOGY ; COPERNICUS ; REGRESSION ; TYCHO ; MOON
资助项目Key Research Program of the Chinese Academy of Sciences[XDPB11-3] ; National Natural Science Foundation of China[41571388]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000504716700135
出版者MDPI
资助机构Key Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/131091]  
专题中国科学院地理科学与资源研究所
通讯作者Cheng, Weiming
作者单位1.CAS Ctr Excellence Comparat Planetol, Hefei 230052, Anhui, Peoples R China
2.Chinese Acad Sci, Jointly Sponsored Beijing Normal Univ & Inst Remo, State Key Lab Remote Sensing Sci, Beijing 100088, Peoples R China
3.Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China
4.Southwest Petr Univ, Sch Civil Engn & Architecture, Chengdu 610500, Sichuan, Peoples R China
5.Chinese Acad Sci, Inst Geochem, Lunar & Planetary Sci Res Ctr, Guiyang 550002, Peoples R China
6.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
8.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Qiangyi,Cheng, Weiming,Yan, Guangjian,et al. A Machine Learning Approach to Crater Classification from Topographic Data[J]. REMOTE SENSING,2019,11(21):30.
APA Liu, Qiangyi,Cheng, Weiming,Yan, Guangjian,Zhao, Yunliang,&Liu, Jianzhong.(2019).A Machine Learning Approach to Crater Classification from Topographic Data.REMOTE SENSING,11(21),30.
MLA Liu, Qiangyi,et al."A Machine Learning Approach to Crater Classification from Topographic Data".REMOTE SENSING 11.21(2019):30.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。