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 (HRSI) 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 paper proposes a progressive pseudo-label learning for cross-domain cloud detection in high-resolution remote sensing (PTDA). Firstly, we propose an online domain-invariant feature guided pseudo-label generation (OPLG) strategy and learning intra-domain unaligned features (LIUF), which effectively integrate domain-invariant features and intra-domain semantics to generate high-quality pseudo-labels at the feature level. LIUF then refines the pseudo-label quality at the pixel level. Secondly, during the model training, pseudo-label constrained intra-domain 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 mIoU 63.99%, 58.14%, 58.82%, and OA 80.03%, 79.36%, 80.49%, respectively. The experimental results show that the proposed method outperforms seven state-of-art 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.