Identify, Guess and Reconstruct: Three Principles for Cloud Removal Task

Remote sensing images serve a significant role in earth observation to tackle climate change and post-disaster reconstruction concerns. However, optical images are obscured by clouds or haze, preventing precise earth observation; hence, cloud removal has been a hot topic among concerned scholars. The objective of this article is to make cloud removal more efficient and explicable by proposing three principles: identifying clouds, guessing objects beneath the clouds, and reconstructing the cloudy area. In addition, a modified dual contrastive learning Generative Adversarial Network is proposed based on these three principles by adding cloud detection and weight sharing strategy to obtain cloud semantics. In particular, we align two datasets by forming a quaternary sample pair that includes not only optical pictures and SAR images, but also region information for a more precise reconstruction. Our experiment results on the integrated dataset reveal the superiority of proposed method over previous cloud removal methods and the effectiveness of added modules through ablation experiments, with PSNR and SSIM values of 26.2 and 0.728, respectively.