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  1. 201

    Deformation Monitoring of Truss Structure Bridge With Time-Series InSAR Analysis by Chuanxu Sun, Gen Li, Zihan Hu, Yifan Wang, Zehua Dong, Tao Zeng

    Published 2025-01-01
    “…In order to solve these problems, an improved PSI method is proposed in this article, which is first to carry out preliminary elevation measurement by a small temporal baseline, use the preliminary elevation results to PS-arcs construction based on 3-D spatial network, and utilize the optimal model coherence coefficient adaptive selection based on constant false alarm rate to carry out the parameter estimation so as to realize the accurate monitoring of deformation of large-span steel truss structure bridges. …”
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    Sentinel-2 Masking CNNs Trained on Physics-Supervised Labels by Efrain Padilla-Zepeda, Kevin Alonso, Raquel De Los Reyes, Deni Torres-Roman, Avi Putri Pertiwi, Tobias Storch

    Published 2025-01-01
    “…Rather than relying on manually labeled data, the proposed method selects high-quality training samples from Python-based atmospheric correction software (PACO), using pixel selection strategies to remove ambiguous or inconsistent labels. …”
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    PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data by Dandan Li, Hailiang Lu, Mercedes E. Paoletti, Juan M. Haut, Juntao Gu, Chao Li, Weipeng Jing

    Published 2025-01-01
    “…Nevertheless, due to the complex terrain of forest areas, it is difficult to ensure the inversion accuracy when training in a specific forested scene and then applying it to other areas. …”
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    Enhancing Aerosol Vertical Distribution Retrieval With Combined LSTM and Transformer Model From OCO-2 O2 A-Band Observations by YuXuan Wang, RuFang Ti, ZhenHai Liu, Xiao Liu, HaiXiao Yu, YiChen Wei, YiZhe Fan, YuYao Wang, HongLian Huang, XiaoBing Sun

    Published 2025-01-01
    “…Furthermore, a physics-based, information-driven band selection method was developed to simplify input data and reduce complexity. …”
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    From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy by Li Wen, Shawn Ryan, Megan Powell, Joanne E. Ling

    Published 2025-07-01
    “…The PCT-level classification achieved an overall accuracy of 93.2%, a kappa coefficient of 0.91, and a Matthews correlation coefficient (MCC) of 0.89, with broader classification levels exceeding 95% accuracy. …”
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