增强 Sentinel-2 图像分辨率：基于卷积与生成式神经网络的先进技术评估
Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks
AI总结：
本文针对 Sentinel-2 森林区域影像，构建对齐优化的数据集，比较 CNN 与增强型 GAN 超分辨率方法，发现 GAN 在清晰度和定量指标上均优于 CNN，验证了其跨土地类型的应用潜力。
摘要：
本文研究了利用先进超分辨率技术将包含光谱信息的 Sentinel-2 波段空间分辨率提升 2 倍的问题。在质量和可行性方面，将最先进的 CNN 模型与增强型 GAN 方法进行了比较。因此，需要一个包含 Sentinel-2 低分辨率图像和相应高分辨率航空正射影像的代表性数据集。文献研究表明，对于所关注的土地类型（森林），没有可行的数据集，因此必须额外生成一个适当的数据集，并考虑精确对齐和图像源优化。结果表明，虽然基于 CNN 的方法能产生令人满意的结果，但往往会产生模糊的图像。相比之下，基于 GAN 的模型不仅能提供清晰细致的图像，而且在定量评估方面表现出更优越的性能，凸显了该框架在所研究的特定土地类型之外的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
