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    • Airborne multidimensional SAR land cover dataset and fusion classification method

    • In the field of remote sensing, experts have constructed the high-resolution and multi-dimensional SAR land classification dataset AIR-MDSARMap, which verifies the application of deep learning algorithms in land classification and provides support for the research of multi-dimensional SAR data applications.
      • role:First author第一作者
      • Affiliation:

        Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

      • Email:nrzheng20@fudan.edu.cn
      • Introduction:郑乃榕,研究方向为合成孔径雷达图像的解译与应用。E-mail: nrzheng20@fudan.edu.cn

      ZHENG Nairong

      1,
      • Affiliation:

        Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

      YANG Zi’an

      1,
      • Affiliation:

        Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

      SHI Xianzheng

      1,
      • Affiliation:

        Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

        School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China

      YANG Hong

      23,
      • Affiliation:

        Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

      SUN Yue

      2,
      • role:Corresponding author通信作者
      • Affiliation:

        Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai 200433, China

      • Email:fengwang@fudan.edu.cn
      • Introduction:王峰,研究方向为智能遥感信息获取。E-mail: fengwang@fudan.edu.cn

      WANG Feng

      1 *
    • Vol. 28, Issue 9, Pages: 2209-2222(2024)  
    • DOI:10.11834/jrs.20242276    

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