STGAN-CR: A Swin Transformer-Enhanced GAN Framework for Effective Cloud Removal in Satellite Imagery
Year: 2024
Type: conference-paper
Abstract: Advancements in satellite remote sensing have enhanced Earth observation, enabling the acquisition of images crucial for various remote sensing applications.However, cloud cover degrades image quality and obstructs surface information.Traditional cloud removal techniques struggle to restore both low-level and high-level features, especially under complex conditions.To address these challenges, we propose STGAN-CR, a novel framework integrating Swin Transformer with Generative Adversarial Networks (GANs) to optimize image detail recovery.Leveraging the Swin Transformer's global modeling capabilities, our method enhances feature extraction and restoration, overcoming limitations of conventional models.We introduce a new evaluation metric focused on scene classification accuracy postde-clouding to better assess practical utility.Extensive experiments and ablation studies show that STGAN-CR outperforms existing models in visual quality and classification performance.These advancements offer an effective solution for enhancing the quality and utility of remote sensing images, balancing the restoration of both low-level and high-level features, and providing more meaningful de-clouded images for downstream applications.
Authors: Hongming Zhu, Zeju Wang, Manxin Xu, Jipeng Zhang, Qin Liu, Bowen Du
Institution: Tongji University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4406255679