Cloud Removal of Optical Remote Sensing Imagery with Multitemporal Sar-Optical Data Using X-Mtgan

Abstract

Optical remote sensing images are inevitably corrupted by clouds during the acquisition process. To reconstruct the missing information contaminated by clouds, this paper introduces a new cloud removal method based on X-fork generative adversarial network with multitemporal data, which can be named X-MTGAN. By utilizing the auxiliary differential image between two imaging times, X-MTGAN can be well trained with multitemporal SAR-optical data. Then, the target optical image is synthesized with an end-to-end generator of the X-MTGAN, which has advantages in capturing change information between two temporal images. Finally, the cloud-free image can be subsequently acquired by replacing cloud-contaminated regions with the simulated image. By utilizing Setinel-1 and Sentinel-2 data, experiments are conducted to validate the feasibility of the proposed approach. Compared with the state-of-the-art methods, the results illustrate that X-MTGAN is visually and quantitatively effective in the removal of clouds, which has favorable applicability and competitive performance.