MTSR-GAN: A Progressive Residual GAN Structure Combined With the Swin-Transformer for Multitask Cross-Sensor Satellite Image Super-Resolution

作者：Hao Han, Wen Du, Ziyi Feng, Tongyu Xu · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2025 · DOI：10.1109/jstars.2025.3631332 · 研究领域：Advanced Image Processing Techniques、Advanced Image Fusion Techniques、Sparse and Compressive Sensing Techniques

In recent years, super-resolution (SR) reconstruction techniques for remote sensing imagery has attracted extensive attention due to its advantages of a low cost and flexible and convenient application. Unlike conventional SR tasks, the super-resolution reconstruction of remote sensing images under real-world conditions is usually accompanied by a large number of objective unfavorable factors, including high levels of noise, cloud occlusion, and variability across different sensors. Consequently, real-scene SR reconstruction constitutes a multi-task problem, yet existing SR approaches seldom address these challenges in a unified framework. To overcome these limitations this study proposes a multi-task super-resolution reconstruction network named MTSR-GAN specifically designed for real-world remote sensing imagery. The proposed network adopts the generative adversarial network(GAN) model's structure, and integrates three dedicated modules within the generator: a deep feature extraction module, a spectral transfer module, and a thin cloud removal module. Within this framework, we design a novel Swin Transformer module to enhance spatial diversity and training stability in image reconstruction. Moreover, we introduce a framework termed the Efficient Feature Aggregation Architecture (EFAA), which ensures effective feature fusion while enabling seamless collaboration among these components. Together, these components enable effective adaptation to cross-sensor SR reconstruction t...