Bilateral Tensor Ring Decomposition for Thick Cloud Removal in Multitemporal Remote Sensing Images

Cloud removal is crucial for enhancing the quality of remote sensing images (RSIs) and broadening their applicability. Tensor decomposition, which extracts latent correlations across multidimensions, has driven the development of various methods for thick cloud removal in multitemporal RSI (MTRSI). However, current tensor decomposition methods are not specifically tailored for MTRSIs, resulting in insufficient correlation representation and requiring high computational costs in processing MTRSIs. In this article, we construct a novel bilateral tensor ring (BTR) decomposition, the first method specifically designed for MTRSIs, which enables a customized representation of spatial, spectral, and temporal correlations with lower computational complexity. The fundamental idea behind BTR decomposition is to effectively distinguish between the weak spatial correlation and the strong spectral–temporal correlation while simultaneously capturing the interaction between these two components. With the support of BTR decomposition, we propose an MTRSI cloud removal model and develop an efficient proximal alternating minimization (PAM)-based algorithm to solve it. In theory, we prove a convergence guarantee for the algorithm. Extensive experimental results verify that our method offers superior cloud removal performance and delivers a 10- to 100-fold acceleration in computational efficiency compared to the state-of-the-art tensor-based methods. The code is available at: https://yubangzheng.github.io.