Toosi et al. (2025) S ³ -ESRGAN: Enhanced Super-Resolution Generative Adversarial Network for Remote Sensing Imagery Spatial Resolution Improvement—An Application Using Sentinel-2 and UAV Images

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-12-08
• Authors: Ahmad Toosi, Farhad Samadzadegan, Farzaneh Dadrass Javan
• DOI: 10.1109/jstars.2025.3640940

Research Groups

This paper introduces S3-ESRGAN, an enhanced super-resolution generative adversarial network, designed to improve the spatial resolution of remote sensing imagery, demonstrated through applications using Sentinel-2 satellite and Unmanned Aerial Vehicle (UAV) images.

Objective

• To develop and apply an enhanced super-resolution generative adversarial network (S3-ESRGAN) for improving the spatial resolution of remote sensing images.
• To demonstrate the effectiveness of S3-ESRGAN using Sentinel-2 satellite and UAV imagery.

Study Configuration

• Spatial Scale: Regional (Sentinel-2) to very local (UAV) imagery.
• Temporal Scale: Not explicitly defined, typically single-image or short-sequence processing for super-resolution.

Methodology and Data

• Models used: S3-ESRGAN (Enhanced Super-Resolution Generative Adversarial Network).
• Data sources: Sentinel-2 satellite images, Unmanned Aerial Vehicle (UAV) images.

Main Results

Contributions

• Introduction of S3-ESRGAN, an enhanced super-resolution generative adversarial network tailored for remote sensing imagery.
• Demonstration of S3-ESRGAN's applicability and potential for improving spatial resolution in diverse remote sensing datasets, including Sentinel-2 and UAV images.

Funding

Citation

@article{Toosi2025S,
  author = {Toosi, Ahmad and Samadzadegan, Farhad and Javan, Farzaneh Dadrass},
  title = {S <sup>3</sup> -ESRGAN: Enhanced Super-Resolution Generative Adversarial Network for Remote Sensing Imagery Spatial Resolution Improvement—An Application Using Sentinel-2 and UAV Images},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3640940},
  url = {https://doi.org/10.1109/jstars.2025.3640940}
}