PTDA: Progressive Pseudo-Label Learning for Cross-Domain Cloud Detection in High-Resolution Remote Sensing

The global cloud detection of high-resolution remote sensing images (HRSIs) is crucial for acquiring high-quality imagery and optimizing data utilization. Traditional cloud detection models, which rely on limited samples and fully supervised learning, struggle to adapt to cross-temporal and cross-spatial domains. While current unsupervised domain adaptation (UDA) methods improve performance in cross-domain cloud detection to some extent, generating high-quality, reliable pseudo-labels remains a significant challenge for global cloud detection. Therefore, this article proposes a progressive pseudo-label learning for cross-domain cloud detection in high-resolution remote sensing (PTDA). First, we propose an online domain-invariant feature-guided pseudo-label generation (OPLG) strategy and learning intradomain unaligned features (LIUFs), which effectively integrate domain-invariant features and intradomain semantics to generate high-quality pseudo-labels at the feature level. LIUF then refines the pseudo-label quality at the pixel level. Second, during the model training, pseudo-label-constrained intradomain feature mining loss (PCIF Loss) is designed to suppress noisy semantic information within the domain, the hole effect of thick/thin clouds, and the noise interference of the contour boundary. Four cloud detection datasets, including MS Cloud (MS), HRC_WHU Cloud (WHU), 95 Cloud (95), and WHUS2-CD + (S2), are grouped into three cross-domain tests, MS2WHU, MS2S2, and WHU295. Our approach achieved the best performance with mean intersection over union (mIoU) of 63.99%, 58.14%, and 58.82%, and overall accuracy (OA) of 80.03%, 79.36%, and 80.49%, respectively. The experimental results show that the proposed method outperforms seven state-of-the-art (SOTA) cross-domain comparison methods. Thus, our method has important application value for cross-domain cloud detection. The available code can be downloaded from https://github.com/gasking/PTDA.