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    • Sea ice and water classification for SWOT wide-swath observations using combined unsupervised and supervised machine learning

    • “The SWOT satellite has achieved the first polar sea ice wide cutting height measurement. Researchers have proposed an ice water classification method for SWOT wide cutting observation data, which optimizes labels through unsupervised machine learning and constructs a multi feature supervised model. The classification accuracy reaches 95%, opening up a new direction for sea ice thickness inversion research.”
      • role：First author第一作者
      • Affiliation：

        School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

      • Email：2401046@stu.neu.edu.cn
      • Introduction：李梦飞，研究方向为冰冻圈遥感和卫星测高。E-mail： 2401046@stu.neu.edu.cn

      LI Mengfei

      1,
      • role：Corresponding author通信作者
      • Affiliation：

        School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

      • Email：zhangshengjun@mail.neu.edu.cn
      • Introduction：张胜军，研究方向为卫星遥感与卫星大地测量学。E-mail： zhangshengjun@mail.neu.edu.cn

      ZHANG Shengjun

      1 * ,
      • Affiliation：

        Liaoning Earthquake Agency, Shenyang 110031, China

      KONG Xiangxue

      2,
      • Affiliation：

        School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

      MU Yunfei

      1,
      • Affiliation：

        School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

      DING Haoyu

      1
    • Vol. 30, Issue 10, Pages: 3149-3169(2026)  

    • DOI：10.11834/jrs.20265521    

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