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April 19, 2026Frontiers in Earth ScienceOpen Access

Cloud detection via frequency-guided spatial modeling and bright surface suppression

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Authors

ZSZhongling SongJilin Meteorological BureauXZXiuqing ZhangHainan Meteorology AdministrationXGXiaobo GaiBeijing Meteorological Bureau

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Implication

Novel framework enhances cloud detection accuracy in remote sensing imagery, suggesting improved environmental monitoring outcomes.

Key Points

  • The study aims to improve cloud detection accuracy in remote sensing by addressing the challenges of optically thin clouds and bright surfaces.
  • Developed a framework integrating frequency-domain information and spatial modeling.
  • Utilized a 2D Fast Fourier Transform for frequency-aware feature extraction.
  • Implemented a Bright Surface Confusion Reducer to suppress false activations on bright surfaces.
  • Applied edge-body cooperative supervision for optimizing segmentation consistency.
  • Achieved MIoU improvements of 3.12%, 1.34%, and 4.24% across three benchmark datasets.
  • Reduced false positive rates by 15.1% on snow and ice surfaces.
  • Generalized effectively across various satellite sensors with spatial resolutions of 10m to 1km.

Cite This Study

Song et al. (2026) studied this question.

synapsesocial.com/papers/69e470a4010ef96374d8d7f1https://doi.org/10.3389/feart.2026.1678496
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