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    • Semi-supervised learning based method for building anomaly detection and evaluation in remote sensing images

    • “For the task of detecting and evaluating building anomalies, researchers propose a dual perspective teacher and category adaptive semi supervised learning method DTCA, which utilizes a small number of labeled samples and a large number of unlabeled samples to improve the model's generalization ability, providing a solution to the problems of high labeling costs and sparse abnormal samples.”
      • role：First author第一作者
      • Affiliation：

        School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

      • Email：liuxy0319@whu.edu.cn
      • Introduction：刘欣怡，副研究员，研究方向为实景三维建模、地表异常检测。E-mail：liuxy0319@whu.edu.cn

      LIU Xinyi

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

        Shandong Provincial Institute of Land Surveying and Mapping, Jinan 100101, China

      • Email：zhliu2022@whu.edu.cn
      • Introduction：刘梓航，研究方向为地表异常遥感检测与评估。E-mail：zhliu2022@whu.edu.cn

      LIU Zihang

      2 * ,
      • Affiliation：

        School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

      ZHANG Yongjun

      1,
      • Affiliation：

        Wuhan Optics Valley ZOYON Science and Technology Co., Ltd., Wuhan 430205, China

      PENG Yuan

      3,
      • Affiliation：

        Wuhan Optics Valley ZOYON Science and Technology Co., Ltd., Wuhan 430205, China

      FENG Jian

      3,
      • Affiliation：

        Guangxi Beibu Gulf Investment Group Co., Ltd., Nanning 530029, China

      LU Yixin

      4
    • Pages: 1-21(2026)  

    • DOI：10.11834/jrs.20266046    

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