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Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning

文献类型：期刊论文

作者Liu, Danyang2,3; Cheng, Weiming2,3,4,5; Qian, Zhen7; Deng, Jiayin2,3; Liu, Jianzhong6; Wang, Xunming1
刊名REMOTE SENSING
出版日期2023-08-01
卷号15期号:16页码:25
关键词geographic information Martian impact craters large geographic extent crater detection algorithm deep learning
DOI10.3390/rs15164036
通讯作者Cheng, Weiming(chengwm@lreis.ac.cn)
英文摘要Detecting impact craters on the Martian surface is a critical component of studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological map-ping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data is proposed. First, the optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff to highlight the boundaries of craters) were used in combination. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using prior geospatial knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65 degrees S and 65 degrees N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research.
WOS关键词IMPACT CRATERS ; MORPHOLOGICAL-CHARACTERISTICS ; LUNAR ; SHAPE
资助项目Chinese Academy of Sciences[XDB41000000] ; National Natural Science Foundation of China[42130110]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001056213100001
出版者MDPI
资助机构Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/196790]  
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.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
5.Chinese Acad Sci, Ctr Excellence Comparat Planetol, Hefei 230052, Peoples R China
6.Chinese Acad Sci, Inst Geochem, Ctr Lunar & Planetary Sci, Guiyang 550081, Peoples R China
7.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Danyang,Cheng, Weiming,Qian, Zhen,et al. Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning[J]. REMOTE SENSING,2023,15(16):25.
APA Liu, Danyang,Cheng, Weiming,Qian, Zhen,Deng, Jiayin,Liu, Jianzhong,&Wang, Xunming.(2023).Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning.REMOTE SENSING,15(16),25.
MLA Liu, Danyang,et al."Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning".REMOTE SENSING 15.16(2023):25.

入库方式： OAI收割

来源：地理科学与资源研究所

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