Feature-Domain Fidelity and Tensor Low-Rank Regularization for Cloud Removal in Remote Sensing Images

The pixel intensity of remote sensing images at different time nodes exhibits significant differences due to factors such as changes in solar illumination angles. Consequently, the previous cloud removal methods, primarily based on the original pixel domain, yield unsatisfactory results. In this paper, considering the sharing of similar features among remote sensing images at different time nodes, we first design a novel feature-domain fidelity that leverages the feature extraction capability of convolution operator, allowing for the precise preservation of intricate details and textures inherent in multi-temporal remote sensing images. Building upon the feature-domain fidelity, we propose a cloud removal model that organically integrates the low fully-connected tensor network rank regularization, which comprehensively captures the spatial-spectral-temporal correlations of multi-temporal remote sensing images. Moreover, we develop an effective algorithm based on proximal alternating minimization to solve the proposed model. Numerical experiments conducted on both simulated and real-world data validate that the proposed method outperforms the compared ones.