LAUSR

Cloud cover presents a major challenge for geoscience research of remote sensing images with thick clouds causing complete obstruction with information loss while thin clouds blurring the ground objects. Deep learning (DL) methods based on convolutional neural networks (CNNs) have recently been introduced to the cloud removal task. However, their performance is hindered by their weak capabilities in contextual information extraction and aggregation. Unfortunately, such capabilities play a vital role in characterizing remote sensing images with complex ground objects. In this work, the conventional cycle-consistent generative adversarial network (CycleGAN) is revitalized from a feature enhancement perspective. More specifically, a saliency enhancement (SE) module is first designed to replace the original CNN module in CycleGAN to re-calibrate channel attention weights to capture detailed information for multi-level feature maps. Furthermore, a high-level feature enhancement (HFE) module is developed to generate contextualized cloud-free features while suppressing cloud components. In particular, HFE is composed of both CNN- and transformer-based modules. The former enhances the local high-level features by employing residual learning and multi-scale strategies, while the latter captures the long-range contextual dependencies with the Swin transformer module to exploit high-level information from a global perspective. Capitalizing on the SE and HFE modules, an effective Cloud-Enhancement GAN, namely Cloud-EGAN, is proposed to accomplish thin and thick cloud removal tasks. Extensive experiments on the RICE and the WHUS2-CR datasets confirm the impressive performance of Cloud-EGAN.