Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis. Issue 5 (14th June 2022)

• Record Type: Journal Article
• Authors: Wang, Yan-Wen; Qin, Cheng-Zhi; Cheng, Wei-Ming; Zhu, A-Xing; Wang, Yu-Jing; Zhu, Liang-Jun
• Is Part Of: Annals of the American Association of Geographers. Volume 112:Issue 5(2022)
• Page Start: 1328
• Page End: 1349
• Publication Date: 2022-06-14
• Subjects: crater detection -- digital terrain analysis -- legacy map -- random forest -- spatial structural information; 撞击坑提取 -- 数字地形分析 -- 旧有地图 -- 随机森林 -- 空间结构信息。; análisis digital del terreno -- bosque aleatorio -- detección de cráteres -- información estructural espacial -- mapa del legado; Geography -- Periodicals; Environmental sciences -- Periodicals; Geography; Electronic journals; Periodicals; 550
• DOI: 10.1080/24694452.2021.1960473
• Languages: English
• ISSNs: 2469-4452

Abstract

Abstract : 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.