A Blind Cloud/Shadow Removal Strategy for Multi-Temporal Remote Sensing Images

For multi-temporal remote sensing (RS) images, the distribution of surface materials is constant concerning time and the same material shows different spectral features at different times. Decomposing the image at each time into an abundance tensor and temporal features, there is a strong similarity between abundance tensors of all time. Based on this observation, we suggest a blind thick cloud/shadow removal model, which exploits the sparsity of the cloud component and the similarity between abundance tensors, achieving both cloud detection and multi-temporal information restoration. Moreover, a mask refinement strategy is designed to pursue the optimal cloud/shadow mask. We develop an efficient algorithm to solve the proposed model based on the augmented Lagrange multiplier method. The results of simulated experiments in different scenarios verify the superiority of the proposed method for thick cloud/shadow removal.