Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis.
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 informatio...
| Publicado en: | Annals of the American Association of Geographers Vol. 112; no. 5; pp. 1328 - 1350 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=157442752&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157442752 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2022 vid: 112 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 157442752 10.1080/24694452.2021.1960473 ppf: 1328 ppct: 22 formats: tig: atl: Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis. aug: au: Wang, Yan-Wen Qin, Cheng-Zhi Cheng, Wei-Ming Zhu, A-Xing Wang, Yu-Jing Zhu, Liang-Jun affil: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China, and College of Resources and Environment, University of Chinese Academy of Sciences, China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China, and College of Resources and Environment, University of Chinese Academy of Sciences, China, and Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application and School of Geography, Nanjing Normal University, China, and Department of Geography, University of Wisconsin–Madison, USA State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, China su: Random forest algorithms Landforms Altimeters Geomorphological mapping sug: subj: Random forest algorithms Landforms Altimeters Geomorphological mapping keyword: crater detection digital terrain analysis legacy map random forest spatial structural information análisis digital del terreno bosque aleatorio detección de cráteres información estructural espacial mapa del legado 撞击坑提取 数字地形分析 旧有地图 空间结构信息。 随机森林 crater detection digital terrain analysis legacy map random forest spatial structural information análisis digital del terreno bosque aleatorio detección de cráteres información estructural espacial mapa del legado 撞击坑提取 数字地形分析 旧有地图 空间结构信息。 随机森林 ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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