Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks
<|reference_start|>Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks: This paper investigates the enhancement of spatial resolution in Sentinel-2 bands that contain spectral information using advanced super-resolution techniques by a factor of 2. State-of-the-art CNN models are compared with enhanced GAN approaches in terms of quality and feasibility. Therefore, a representative dataset comprising Sentinel-2 low-resolution images and corresponding high-resolution aerial orthophotos is required. Literature study reveals no feasible dataset for the land type of interest (forests), for which reason an adequate dataset had to be generated in addition, accounting for accurate alignment and image source optimization. The results reveal that while CNN-based approaches produce satisfactory outcomes, they tend to yield blurry images. In contrast, GAN-based models not only provide clear and detailed images, but also demonstrate superior performance in terms of quantitative assessment, underlying the potential of the framework beyond the specific land type investigated.<|reference_end|>
@article{kramer2024enhancing,
    title={Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques
  based on Convolutional and Generative Neural Networks},
    author={Patrick Kramer, Alexander Steinhardt, Barbara Pedretscher},
    journal={arXiv preprint arXiv:2410.00516},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00516},
    primaryClass={eess.IV cs.AI cs.CV}
}