Edge-SAR-Assisted Multimodal Fusion for Enhanced Cloud Removal.

In Earth observation activities, cloud severely affects the interpretation of high-resolution imagery, generated by optical satellites. Therefore, removing clouds from optical imagery becomes a topic of interest in the remote sensing field. Currently, most methods use auxiliary synthetic aperture radar (SAR) images to reconstruct optical images by merging SAR and optical images into a deep learning network. However, the speckle noise of the SAR image is not taken into consideration during feature fusion processing, leading to blurry edges in the reconstructed optical images. To get fine-grained optical images, we propose a novel cloud removal framework based on the edge fusion of SAR and optical images. First, the edge feature of SAR images is extracted by the GRHED. As the prior knowledge, it can provide fine-grained edge information for subsequent reconstruction work. Then channels from three modal data are stacked to guide the reconstruction of optical images by exploiting their correlations and interactions. Furthermore, a structural similarity (SSIM) loss function is introduced to optimize the training network and improve the coherence of the image structure. Experimental results confirm its advantages on the SEN12MS-CR dataset.