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Self-supervised representation learning for cloud detection using Sentinel-2 images
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DOI：10.1016/j.rse.2025.115205.png)
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Abstract
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• Two self-supervised approaches are leveraged for cloud and cloud shadow detection. • MoCo and DeepCluster outperformed industry standards such as FMask and Sen2Cor with few labels. • Reliable performance was obtained for both methods using 25 % of annotations in the training sets. • MoCo and DeepCluster handle clouds better than cloud shadows.
Keywords:
Cloud and cloud shadow
Contrastive learning
MoCo
Deep clustering
DeepCluster
WHUS2–CD+
CloudSEN12
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