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    • Automatic anchor-free convolutional neural network method for recognizing small-scale lunar impact craters

    • In the field of lunar surface impact crater recognition, experts have proposed an anchor free deep convolutional neural network automatic recognition method based on transfer learning, which effectively solves the problem of small-scale impact crater recognition and provides a new solution for lunar surface dating research.
      • role:First author第一作者
      • Affiliation:

        School of Earth Resources, China University of Geosciences, Wuhan 430074, China

        College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

        Space Optoelectronic Measurement and Perception Lab., Beijing Institute of Control Engineering, Beijing 100190, China

      • Email:17686101523@163.com
      • Introduction:张梓璇,研究方向为月表环形构造提取与分析。E-mail: 17686101523@163.com

      ZHANG Zixuan

      123,
      • role:Corresponding author通信作者
      • Affiliation:

        College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

        Space Optoelectronic Measurement and Perception Lab., Beijing Institute of Control Engineering, Beijing 100190, China

      • Email:jtyang@sdust.edu.cn
      • Introduction:杨俊涛,研究方向为激光雷达技术与应用、农业遥感、月球与行星遥感。E-mail: jtyang@sdust.edu.cn

      YANG Juntao

      23 * ,
      • Affiliation:

        Space Optoelectronic Measurement and Perception Lab., Beijing Institute of Control Engineering, Beijing 100190, China

      LI Lin

      3,
      • Affiliation:

        College of Earth Sciences and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

      ZHANG Shuowei

      4,
      • Affiliation:

        College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

      YANG Ziyi

      2,
      • Affiliation:

        Space Optoelectronic Measurement and Perception Lab., Beijing Institute of Control Engineering, Beijing 100190, China

      MA Yuechao

      3
    • Vol. 29, Issue 2, Pages: 429-441(2025)  
    • DOI:10.11834/jrs.20243206    

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