Spatial-spectral-temporal connective tensor network decomposition for thick cloud removal

Thick cloud removal is a fundamental and challenging remote sensing image processing problem, which is beneficial to subsequent analysis and application. Fortunately, multi-temporal remote sensing images (MTRSIs) contain highly correlated spatial–spectral–temporal (SST) information that makes it possible to solve the thick cloud removal problem. To explore the rich SST relationships of MTRSIs effectively and efficiently, this paper proposes a semi-blind thick cloud removal method via SST connective tensor network decomposition (SSTC-CR). Specifically, the proposed model performs the subspace representation along the spectral mode of the image at each time node, and then introduces the tensor network decomposition to characterize the intrinsic relationship of the fourth-order tensor consisting of all intrinsic images under subspace representation. Besides, we develop a proximal alternating minimization-based algorithm to tackle the newly-built thick cloud removal model. In the developed algorithm, we integrate the cloud removal and mask refinement by updating optimization variables and the cloud masks alternately. Extensive numerical experiments on simulated and real-world MTRSIs substantiate that the proposed method outperforms other state-of-the-art tensor modeling and deep learning-based methods, especially for color protection. The code is available at https://github.com/zhaoxile/SSTC_CR.