中国科学:地球科学(英文版)2024,Vol.67Issue(6) :1719-1742.DOI:10.1007/s11430-023-1284-9

Impact crater recognition methods:A review

Dong CHEN Fan HU Liqiang ZHANG Yunzhao WU Jianli DU Jiju PEETHAMBARAN
中国科学:地球科学(英文版)2024,Vol.67Issue(6) :1719-1742.DOI:10.1007/s11430-023-1284-9

Impact crater recognition methods:A review

Dong CHEN 1Fan HU 1Liqiang ZHANG 2Yunzhao WU 3Jianli DU 4Jiju PEETHAMBARAN5
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作者信息

  • 1. College of Civil Engineering,Nanjing Forestry University,Nanjing 210037,China
  • 2. Department of Geography,State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
  • 3. Purple Mountain Observatory,Chinese Academy of Sciences,Nanjing 210023,China
  • 4. Purple Mountain Observatory,Chinese Academy of Sciences,Nanjing 210023,China;Key Laboratory of Space Object and Debris Observation,Chinese Academy of Sciences,Nanjing 210023,China
  • 5. Department of Mathematics and Computing Science,Saint Mary's University,Halifax,NS B3H 3C3,Canada
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Abstract

Impact craters are formed due to the high-speed collisions between small to medium-sized celestial bodies.Impact is the most significant driving force in the evolution of celestial bodies,and the impact craters provide crucial insights into the formation,evolution,and impact history of celestial bodies.In this paper,we present a detailed review of the characteristics of impact craters,impact crater remote sensing data,recognition algorithms,and applications related to impact craters.We first provide a detailed description of the geometric texture,illumination,and morphology characteristics observed in remote sensing data of craters.Then we summarize the remote sensing data and cataloging databases for the four terrestrial planets(i.e.,the Moon,Mars,Mercury,and Venus),as well as the impact craters on Ceres.Subsequently,we study the advancement achieved in the traditional methods,machine learning methods,and deep learning methods applied to the classification,segmentation,and recognition of impact craters.Furthermore,based on the analysis results,we discuss the existing challenges in impact crater recognition and suggest some solutions.Finally,we explore the implementation of impact crater detection algorithms and provide a forward-looking perspective.

Key words

Terrestrial planets/Deep space exploration/Impact crater/Impact crater recognition/Deep learning

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基金项目

National Natural Science Foundation of China(41925006)

National Natural Science Foundation of China(12003075)

National Natural Science Foundation of China(42371383)

National Natural Science Foundation of China(42271450)

出版年

2024
中国科学:地球科学(英文版)
中国科学院

中国科学:地球科学(英文版)

影响因子:1.002
ISSN:1674-7313
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