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Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis

文献类型：期刊论文

作者Wang, Yan-Wen4,5; Qin, Cheng-Zhi1,3,4,5; Cheng, Wei-Ming4,5; Zhu, A-Xing1,2,3,5; Wang, Yu-Jing4,5; Zhu, Liang-Jun5
刊名ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
出版日期2021-08-12
页码22
关键词crater detection digital terrain analysis legacy map random forest spatial structural information
ISSN号2469-4452
DOI10.1080/24694452.2021.1960473
通讯作者Wang, Yan-Wen(y.wang-4@utwente.nl)
英文摘要Detection of craters is important not only for planetary research but also for engineering applications. Although the existing crater detection approaches (CDAs) based on terrain analysis consider the topographic information of craters, they do not take into account the spatial structural information of real craters. In this article, we propose an automatic crater detection approach by training random forest classifiers with data from legacy crater map and spatial structural information of craters derived from digital terrain analysis. In the proposed two-stage approach, first, the cells in a legacy crater map are used as samples to train the random forest classifier at a cell level based on multiscale landform element information. This trained classifier is then applied to identify crater candidates in the areas of interest. Second, an object-level random forest classifier is trained with radial elevation profiles of craters and is subsequently applied to evaluate whether each crater candidate is real. A case study using the Lunar Orbiter Laser Altimeter crater map and lunar digital elevation model with 500-m resolution showed that the proposed approach performs better than AutoCrat (a representative CDA), and can mine the implicit expert knowledge on the spatial structures of real craters from legacy crater maps. The proposed approach could be extended to extract other geomorphologic types in similar application situations.
WOS关键词LUNAR IMPACT CRATERS ; TOPOGRAPHIC POSITION ; SOIL MAPS ; CLASSIFICATION ; RECOGNITION ; CHANGE-1 ; SYSTEM
资助项目Chinese Academy of Sciences[XDB41020400] ; National Natural Science Foundation of China[41422109]
WOS研究方向Geography
语种英语
WOS记录号WOS:000708730400001
出版者ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
资助机构Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/166891]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Yan-Wen
作者单位1.Nanjing Normal Univ, Sch Geog, Nanjing, Peoples R China
2.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
3.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yan-Wen,Qin, Cheng-Zhi,Cheng, Wei-Ming,et al. Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis[J]. ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,2021:22.
APA Wang, Yan-Wen,Qin, Cheng-Zhi,Cheng, Wei-Ming,Zhu, A-Xing,Wang, Yu-Jing,&Zhu, Liang-Jun.(2021).Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis.ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS,22.
MLA Wang, Yan-Wen,et al."Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis".ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS (2021):22.

入库方式： OAI收割

来源：地理科学与资源研究所

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