Spectral-Temporal Low-Rank Regularization with Deep Prior for Thick Cloud Removal

Remote sensing (RS) images are unavoidably contaminated by thick clouds, greatly limiting their subsequent application and exploration. Most existing conventional thick cloud removal methods are based on hand-crafted priors, which utilize the low-rank or smoothness property to regularize the latent RS images. However, these hand-crafted priors are failed to describe the rich structure that many RS images exhibit. Deep learning (DL) methods achieve their performance owing to extensive labeled training data while large-scale labeled data are expensive to acquire in the RS scene. In this paper, a thick cloud removal method named Spectral-Temporal Low-Rank regularization with Deep Prior (STLR-DP) is proposed to tackle these issues, solely using a single cloud-contaminated image without any extra external training data or pre-trained models, which utilizes an untrained neural network to capture the rich characteristic of RS images rather than hand-crafted priors. The spectral-temporal low-rank regularization is further incorporated into the model to avoid the over-fitting problem. Benefiting from the deep intrinsic image characteristic captured by the neural network and its self-supervised nature, our method can effectively simultaneously reconstruct the contour and details of contaminated regions, and can be adaptive to various RS images with strong generalization ability. Experimental results on simulated and real datasets demonstrate that the proposed STLR-DP method outperforms the representative thick cloud removal and tensor completion methods.