Forestry and Agriculture | Views:1070Downloads:2856CSCD:2

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    • JAM-R-CNN deep learning network model for remote sensing recognition of terraced fields

    • “In the field of remote sensing recognition, experts have proposed the JAM-R-CNN deep network model, which effectively improves the accuracy of terrace recognition and has practical value.”
      • role：First author第一作者
      • Affiliation：

        College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

        Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China

      • Email：xjy959@mails.ccnu.edu.cn
      • Introduction：谢君洋，研究方向为农业遥感。E-mail： xjy959@mails.ccnu.edu.cn

      XIE Junyang

      12,
      • Affiliation：

        College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

        Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China

      LIN Anqi

      12,
      • role：Corresponding author通信作者
      • Affiliation：

        College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

        Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China

      • Email：haowu@ccnu.edu.cn
      • Introduction：吴浩，研究方向为地理信息科学与遥感。E-mail： haowu@ccnu.edu.cn

      WU Hao

      12 * ,
      • Affiliation：

        College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

        Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China

      WU Ziwei

      12,
      • Affiliation：

        Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      WU Wenbin

      3,
      • Affiliation：

        Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

      YU Qiangyi

      3
    • Vol. 28, Issue 12, Pages: 3136-3146(2024)  

    • DOI：10.11834/jrs.20233126    

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