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

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.