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    • Mamba-CSNet: Joint CNN and Mamba State-Space Modeling for Hyperspectral Image Compressive Sensing

    • Hyperspectral image compression faces storage and transmission challenges. Mamba CSNet integrates CNN and Mamba's feature enhancement module and two-stage quantization encoding strategy, achieving a PSNR of 38.024 dB at a 1% sampling rate and reducing computational complexity by 11.1%, providing a solution for efficient compression of spaceborne hyperspectral data.
      • role:First author第一作者
      • Affiliation:

        State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China

      • Email:jiajunchen@whu.edu.cn
      • Introduction:陈嘉骏,研究方向为高光谱图像压缩与重建。E-mail:jiajunchen@whu.edu.cn

      CHEN Jiajun

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

        School of Artificial Intelligence, Wuhan University, Wuhan 430072, China

      • Email:jing@whu.edu.cn
      • Introduction:肖晶,研究方向为高精度卫星遥感实时智能服务。E-mail:jing@whu.edu.cn

      XIAO Jing

      2 * ,
      • Affiliation:

        Hangzhou Institute, Xidian University, Hangzhou 311231, China

      LIAO Liang

      3,
      • Affiliation:

        State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China

        Hubei Luojia Laboratory, Wuhan 430079, China

      WANG Mi

      14
    • Vol. 30, Issue 9, Pages: 2811-2824(2026)  
    • DOI:10.11834/jrs.20266056    

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