Removal of Optically Thick Clouds From High-Resolution Satellite Imagery Using Dictionary Group Learning and Interdictionary Nonlocal Joint Sparse Coding

In this paper, we propose a method for cloud removal in a cloud-contaminated high-resolution (HR) optical satellite image with two kinds of auxiliary images of different types: a low-resolution (LR) optical satellite composite image and a synthetic aperture radar (SAR) image. In the proposed method, we assume that cloud-contaminated and cloud-free regions have been detected accurately, then dictionary group learning (DGL) is used to establish structure correspondences between HR, LR, and SAR data from cloud-free patches, while interdictionary nonlocal joint sparse coding (INJSC) is used to estimate the universal representation coefficients of patches contaminated by clouds, and finally, cloud-contaminated HR patches can be reconstructed with their universal coefficients and the HR dictionary learned from DGL process. In this way, the missing information in the cloud-contaminated HR image can be reconstructed patch by patch. The proposed method is tested on a series of experiments on both simulated and real data. Experimental results show that both DGL and INJSC are beneficial to better reconstructing the missing information. This method is also compared against our previous work on the same topic, which adopted dictionary pair learning (DPL) and sparse coding (SC) to recover the missing information and achieved state-of-the-art performance at that time. The comparison shows that the method proposed in this paper significantly outperforms the previous one.