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This letter presents a cloud removal method for reconstructing the missing information in cloud-contaminated regions of a high-resolution (HR) optical satellite image (HRI) using two types of auxiliary images, i.e., a low-resolution (LR) optical satellite composite image (LRI) and a synthetic aperture radar (SAR) image. The LRI contributes low-frequency information, and the SAR image contributes high-frequency information for restoring the HRI. The approach is implemented using structure correspondences established by sparse representation. Specifically, two dictionary pairs are trained jointly: One pair is generated from the HRI and LRI gradient image patches, and the other is generated from the HRI and SAR gradient image patches. Experimental reconstructions of cloud-contaminated regions in HR Thematic Mapper images are performed using three types of auxiliary images, i.e., MODIS 16-day composite only, SAR only, and both MODIS composite and SAR, respectively. It is shown that the MODIS composite or the SAR data alone are not sufficient to restore the missing HR information, whereas the combination of the two types of data can provide both low- and high-frequency information. The proposed approach can achieve a highly accurate result and has potential in areas where land-cover change may occur.
Huang et al. (Thu,) studied this question.