Cloud Removal Method for Remote Sensing Images Based on SAR-Guided Conditional Diffusion Model and Style Adaptation

Cloud cover poses a major challenge to the usability of optical remote sensing images, particularly under thick clouds. This article proposes SAR-DiffCloud, a high-fidelity cloud removal framework that integrates multiband synthetic aperture radar (SAR) guidance with a conditional diffusion model and an adaptive style transfer mechanism. By leveraging the cloud-penetrating characteristics of Ka, X, and C-band SAR data, the model extracts deep scattering features via principal component analysis and fuses them with optical features through a bidirectional fusion architecture. An adaptive style transfer module based on optimal transport theory is introduced to ensure spectral consistency between generated and cloud-free regions. Extensive experiments on the AIR-MD-SAR-Map and OGSOD datasets demonstrate that SAR-DiffCloud significantly outperforms existing methods across structural, spectral, and perceptual quality metrics, particularly under extreme cloud coverage. The results confirm the effectiveness of multimodal guidance and style-aware generation for robust remote sensing image restoration.