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    • A multiscale segment anything model with fine-tuning and high-frequency enhancement for semantic segmentation of remote sensing images

    • “The segmentation model SAM is widely used in visual tasks, but there are obvious limitations when transferring to semantic segmentation of remote sensing images. To solve the above problems, the research team proposes a multi-scale SAM remote sensing image semantic segmentation algorithm that integrates fine-tuning and high-frequency enhancement, providing a solution to the problems of blurred ground boundaries and cross domain feature offset in remote sensing images.”
      • role：First author第一作者
      • Affiliation：

        School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

      • Email：202412640323@nuist.edu.cn
      • Introduction：董雨晴，研究方向为高分辨率遥感语义变化检测。E-mail： 202412640323@nuist.edu.cn

      DONG Yuqing

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

        School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

      • Email：ldsh1201@163.com
      • Introduction：刘东升，研究方向为遥感图像处理、地理空间智能。E-mail： ldsh1201@163.com

      LIU Dongsheng

      1 * ,
      • Affiliation：

        School of Geography and Bioinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

      ZHOU Xinxin

      2,
      • Affiliation：

        Beijing Supermap Software Co., Ltd., Beijing 100015, China

      Lyu Hongchen

      3,
      • Affiliation：

        School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

      LIN Fenfang

      1,
      • Affiliation：

        School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

      HUANG Zhiming

      1
    • Vol. 30, Issue 10, Pages: 3170-3183(2026)  

    • DOI：10.11834/jrs.20266146    

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