STGAN-CR: A Semantics-Aware Cloud Removal Network Integrating Swin Transformer and GANs for Remote Sensing Applications

Hongming Zhu, Zeju Wang, Manxin Xu, Jinfeng Jiang, Jipeng Zhang, Hongfei Fan, Qin Liu, Bowen Du

Abstract: Advancements in satellite remote sensing have enhanced Earth observation, enabling the acquisition of images crucial for various applications. However, cloud cover degrades image quality and obstructs both low-level details and high-level semantic information, impacting downstream tasks like scene classification. Existing methods often fail to balance these features, particularly under complex conditions. To address these challenges, we propose STGAN-CR, a semantics-aware cloud removal framework that integrates Swin Transformer [1] with Generative Adversarial Networks (GANs) to optimize both detail recovery and semantic preservation. Leveraging the Swin Transformer’s global modeling capabilities, STGAN-CR excels in capturing complex spatial dependencies and restoring semantically meaningful features. To better evaluate practical utility, we introduce a novel evaluation metric based on scene classification accuracy, directly reflecting the restoration of high-level semantic information. Extensive experiments and ablation studies demonstrate that STGAN-CR outperforms existing models in both visual quality and semantic understanding, providing robust de-clouded images for downstream remote sensing applications.

External IDs:doi:10.1142/s0218194025410074