Enhancing urban monitoring in cloudy conditions: a novel framework for synergizing cloud-contaminated optical and PolSAR data

Cloud contamination impedes timely and accurate urban monitoring via optical remote sensing in numerous emergent cloudy situations, such as rainstorms and flood responses. Synthetic aperture radar (SAR) offers advantages because of its all-weather capability but has geometric backscattering constraints such as layover and foreshortening. The complementary information from these modalities highlights the importance of fusing optical and SAR data. However, most existing fusion methods are tailored for cloud-free scenarios, overlooking the challenge of cloud contamination. This study develops a novel fusion framework, SAR-optical dictionary learning (SODL), to synergize cloud-contaminated optical data and polarimetric SAR data, with an example of estimating urban impervious surfaces (UIS). SODL avoids cloud interference by constructing a coupled SAR-optical dictionary space with specifically designed constraints, effectively maximizing the discriminative capabilities of both data sources. The experimental results across subtropical China demonstrate the robust performance of SODL, with improvements in UIS estimation of up to 33.74% in overall accuracy (OA) compared with traditional and deep learning fusion methods. Furthermore, SAR and optical fusion with SODL significantly outperform the results using SAR or optical images alone, increasing the OA by up to 45.71%. These outcomes underscore the promising potential of SODL in urban monitoring in cloudy regions.