CloudSeg: A multi-modal learning framework for robust land cover mapping under cloudy conditions

Cloud coverage poses a significant challenge to optical image interpretation, degrading ground information on Earth’s surface. Synthetic aperture radar (SAR), with its ability to penetrate clouds, provides supplementary information to optical data. However, existing optical-SAR fusion methods predominantly focus on cloud-free scenarios, neglecting the practical challenge of semantic segmentation under cloudy conditions. To tackle this issue, we propose CloudSeg, a novel framework tailored for land cover mapping in the presence of clouds. It addresses the challenges posed by cloud cover from two aspects: reducing semantic ambiguity in areas of the cloudy image that are obscured by clouds and enhancing effective information in the unobstructed portions. Specifically, CloudSeg employs a multi-task learning strategy to simultaneously handle low-level visual task and high-level semantic understanding task, mitigating the semantic ambiguity caused by cloud cover by acquiring discriminative features through an auxiliary cloud removal task. Additionally, CloudSeg incorporates a knowledge distillation strategy, which utilizes the knowledge learned by the teacher network under cloud-free conditions to guide the student network to overcome the interference of cloud-covered areas, enhancing the valuable information from the unobstructed parts of cloud-covered images. Extensive experiments conducted on two datasets, M3M-CR and WHU-OPT-SAR, demonstrate the effectiveness and superiority of the proposed CloudSeg method for land cover mapping under cloudy conditions. Specifically, CloudSeg outperforms the state-of-the-art competitors by 3.16% in terms of mIoU on M3M-CR and by 5.56% on WHU-OPT-SAR, highlighting its substantial advantages for analyzing regions frequently obscured by clouds. Codes are available at https://github.com/xufangchn/CloudSeg.