Density-Aware Cloud Removal of Remote Sensing Imagery Using a Global–Local Fusion Transformer

Cloud cover poses a significant challenge in remote sensing image processing, affecting the extraction and analysis of terrestrial features. Despite advancements in multitemporal cloud removal methods, single-image declouding remains crucial for emergency response and disaster management, where rapid acquisition of cloud-free imagery is essential. Traditional approaches often rely on synthetic aperture radar (SAR) or cloud masks as guidance for cloud removal, introducing additional complexities and dependencies on extensive data. To address these limitations, we propose a density-aware cloud removal using a global-local fusion Transformer (DCR-GLFT), which leverages density information as guidance and does not rely on extensive data. Specifically, our method employs density labels to guide the cloud removal process through two primary stages: cloud density estimation and density-guided cloud removal. A cloud density classifier is proposed in the first stage, trained with roughly estimated ground truth, to generate density labels for guiding subsequent removal processes. The second stage integrates cloud density information with cloud-ground image features using a Transformer-based network, enabling precise and nuanced cloud removal while preserving underlying surface details through the integration of both global and local features. The proposed method achieved the state-of-the-art results (peak signal-to-noise ratio (PSNR) of 28.93 and structural similarity index measure (SSIM) of 0.84) on the renowned cloud-removal dataset SEN12MS-CR, even without utilizing SAR data for guidance. This accomplishment highlights its significant advancement in the single-image cloud removal task. Our code will be made available at https://github.com/ruiquan1214/DCR-GLFT.git.