Multi-feature fusion network for efficient cloud removal using SAR-optical image fusion

Clouds in optical images are inevitable and can adversely affect subsequent analysis. Given that SAR imagery remains unaffected by cloud cover, many cloud removal methods involve the fusion of SAR and optical imagery. Unfortunately, existing methods for cloud removal through SAR-optical image fusion are computationally intensive and time-consuming, limiting their practical application. To address these challenges, this paper proposes a novel multi-feature fusion network (MFFNet) for SAR-optical image fusion, aiming to remove clouds from optical images effectively. The proposed method was applied to global and all-season Sentinel-1 and Sentinel-2 images. Quantitative experiments demonstrate that MFFNet achieves high accuracy and efficiency. Specifically, our method obtains an SSIM value of 87.10 and a speed of 25.97 FPS.