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<p>Remote sensing images are indispensable for continuous environmental monitoring and Earth observations. However, cloud occlusion can severely degrade image quality, posing a significant challenge for the accurate extraction of ground information. Existing cloud removal techniques often suffer from incomplete cloud removal, artifacts, and color distortions. Owing to the scarcity of sequential data, the effective utilization of temporal information to enhance cloud removal performance poses a challenge. Therefore, we propose a cloud removal method based on cloud evolution simulation. This method is applicable to all paired cloud datasets, enabling the construction of cloud evolution time-series in the absence of actual temporal information. We embed temporal information from the sequence into the Temporal U-Net to achieve more accurate cloud predictions. We conducted extensive experiments on RICE and T-CLOUD datasets. The results demonstrate that our approach significantly improves the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared with existing methods.</p>

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Enhanced cloud removal via temporal U-Net and cloud cover evolution simulation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月6日

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  • Qingwei Tong,
  • Leiguang Wang,
  • Qinling Dai,
  • Chen Zheng,
  • Fangrong Zhou

摘要

Remote sensing images are indispensable for continuous environmental monitoring and Earth observations. However, cloud occlusion can severely degrade image quality, posing a significant challenge for the accurate extraction of ground information. Existing cloud removal techniques often suffer from incomplete cloud removal, artifacts, and color distortions. Owing to the scarcity of sequential data, the effective utilization of temporal information to enhance cloud removal performance poses a challenge. Therefore, we propose a cloud removal method based on cloud evolution simulation. This method is applicable to all paired cloud datasets, enabling the construction of cloud evolution time-series in the absence of actual temporal information. We embed temporal information from the sequence into the Temporal U-Net to achieve more accurate cloud predictions. We conducted extensive experiments on RICE and T-CLOUD datasets. The results demonstrate that our approach significantly improves the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared with existing methods.