Sentinel-2 Super-Resolution with Real-ESRGAN using Satellite and Aerial Image Pairs and Color Correction Techniques

Improving the spatial resolution of satellite images offers considerable potential for a wide range of remote sensing applications. This study investigates the use of an Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) to improve the resolution of Sentinel-2 satellite imagery, using high-resolution digital orthophotos as ground truth for fine-tuning. The Real-ESRGAN model, which was initially trained on synthetic data, is further refined by combining Sentinel-2 and orthophoto image pairs to achieve a 4-fold upscaling. In the fine-tuning process, a color correction strategy is used to minimize differences due to different sensors. Although visual inspections show that the fine-tuned Real-ESRGAN images appear sharper and more detailed than those generated by conventional interpolation methods, the structural similarity index (SSIM) shows a lower agreement compared to these methods. This suggests that although the fine-tuned images are visually appealing, they present challenges in objective metric evaluation due to the different sensor characteristics and illumination conditions. Nevertheless, this method offers a promising solution for high-resolution remote sensing applications, especially when visual clarity and detail are paramount.