Spectral–Temporal Consistency Prior for Cloud Removal From Remote Sensing Images

Thick cloud removal for multitemporal remote sensing images (MTRSIs) is a necessary preprocessing step for subsequent applications. Existing methods for cloud removal ignore spectral–temporal consistency prior (STCP), such as smooth regions of different time and bands existing in the same spatial location. To address this problem, we propose a factor-based group sparsity regularization within the low-rank tensor factorization (LRTF) framework and theoretically prove that it can characterize the STCP in MTRSIs. Based on this regularization, we construct a cloud removal model for MTRSIs. On one hand, the introduction of STCP enables the model to achieve superior cloud removal performance. On the other hand, regularization on small-sized factors rather than on the original data enables the model to have extremely low computational complexity. To solve this model, we develop a proximal alternating minimization (PAM)-based algorithm, in which we integrate a mask acquisition method based on separated cloud and shadow components. Comparative experiments using both simulated and real data demonstrate that the proposed method outperforms recent mask-unknown and mask-known methods in terms of performance and efficiency.