融合多时相与高分辨率数据，提升哨兵-2影像至2.5米分辨率
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
- 结合单图与多图超分，利用时间序列与高分辨先验
- 将哨兵-2影像提升至2.5米地面采样距离
- 适用于城市地表分类，优于现有超分方法
超分辨率旨在通过重建高频细节提升卫星图像分辨率，超越简单的上采样。这对哨兵-2这类提供免费、频繁覆盖但分辨率较粗的地球观测任务尤为重要。其像素足迹过大，难以捕捉房屋、道路或树篱等小尺度特征。为此，我们提出SEN4X——一种融合单图与多图超分优势的混合架构。该方法结合多次哨兵-2观测的时间过采样与来自高分辨率Pléiades Neo数据的学习先验，将哨兵-2影像升级至2.5米地面采样距离。我们在越南河内进行的城市地表分类测试表明，超分辨率结果显著优于当前最优超分基线。
原文摘要 · Abstract (English)
Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond naïve upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages of single-image and multi-image techniques. It combines temporal oversampling from repeated Sentinel-2 acquisitions with a learned prior from high-resolution Pléiades Neo data. In doing so, SEN4X upgrades Sentinel-2 imagery to 2.5 m ground sampling distance. We test the super-resolved images on urban land-cover classification in Hanoi, Vietnam. We find that they lead to a significant performance improvement over state-of-the-art super-resolution baselines.
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。