Cloud Removal Based on SAR-Optical Remote Sensing Data Fusion via a Two-Flow Network

Abstract

Optical remote sensing imagery plays an important role in observing the Earth's surface today. However, it is not easy to obtain complete multi-temporal optical remote sensing images because of the cloud cover, how reconstructing cloudfree optical images has become a big challenge task in recent years. Inspired by the remote sensing fusion methods based on the convolutional neural network model, we propose a two-flow network to remove clouds from optical images. And in the proposed method, synthetic aperture radar (SAR) images are used as auxiliary data to guide optical image reconstruction, which is not influenced by cloud cover. Additionally, a novel loss function called content loss is introduced to improve image quality. The ablation experiment of the loss function also proves that content loss is indeed effective. To more in line with real situation, the network is trained, tested and validated on the SEN12MS-CR dataset, which is a global real cloud-removal dataset. The experimental results show that the proposed method is better than other the-state-of-art (SOTA) methods in many indicators (RMSE, SSIM, SAM, PSNR).