Under CopyrightKleebauer, MaximilianMaximilianKleebauerMarz, ChristopherChristopherMarzHorst, DanielDanielHorst2024-10-212025-05-132024-10-212024-09-18https://doi.org/10.24406/h-477823https://publica.fraunhofer.de/handle/publica/47782310.13140/RG.2.2.22240.7040510.24406/h-477823Improving 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.enSuper-ResolutionReal-ESRGANRemote SensingSentinel-2 imageryColor CorrectionPan-sharpeningDigital othophotosStructural similarity indexposter