用深度学习提升哨兵2号遥感图像分辨率，生成更清晰的细节。
Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks
- 对比卷积与生成对抗网络，提升图像空间分辨率2倍。
- 生成模型产出更清晰、细节更丰富的图像，定量指标更优。
- 专为森林区域构建新数据集，支持后续遥感研究。
本文研究基于卷积神经网络与生成式神经网络的先进超分辨率技术，将哨兵2号含光谱信息波段的空间分辨率提升2倍。为评估模型性能，需使用高精度对齐的低分辨率哨兵2号图像与对应高分辨率航空正射影像组成的代表性数据集。现有文献中缺乏适用于森林地类的可行数据集，因此本文额外构建了符合要求的数据集，确保图像源优化与精准配准。实验结果表明，尽管基于CNN的方法可获得合理效果，但图像常呈现模糊；而基于GAN的模型不仅生成更清晰、细节更丰富的图像，且在定量评估中表现更优，验证了该框架在特定地类之外也具备广泛应用潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。