Fusing Global Structural and Local Deep Features for Thick Cloud Removal in Multitemporal Remote Sensing Images

Optical remote sensing images are inevitably affected by thick cloud cover, leading to information missing, which seriously hinders subsequent Earth observation tasks. Existing cloud removal methods typically focus on extracting either global or local features, but lack effective fusion of these two aspects, resulting in deficiencies in recovering global structure and fine details. However, fusing multiple features is challenging for single-driven methods due to the diversity of images and clouds. To this end, this article proposes a novel joint-driven cloud removal framework that can simultaneously exploit the global structural and local deep features of both the image and cloud components. Specifically, in our joint-driven scheme, we devise a flexible model-driven low-rank group sparse decomposition method, in which the global temporal-spectral correlations and shared sparse features of multitemporal remote sensing images and clouds from different scenes are effectively captured. To fully account for the differences in the local features of images and clouds from different scenes, we devise effective data-driven dual self-supervised networks to adaptively learn their local deep features. Additionally, we design an efficient half-quadratic splitting algorithm to iteratively optimize the joint-driven model, fostering mutual enhancement between the two and achieving further performance improvements. Experimental results show that the proposed method outperforms existing thick cloud removal approaches in both simulated and real-world datasets, under varying factors such as spectral bands, cloud coverage, time nodes, and spatial resolutions.