不规则采样Sentinel-2时间序列的跨传感器超分辨率
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series
AI总结：
本研究扩展SRDiff和HighRes-net等深度超分辨率算法处理不规则采样的Sentinel-2时间序列，引入BreizhSR数据集，证明多图像提升性能并探讨光谱保真度与感知质量的权衡。
摘要：
卫星成像通常在采集频率和图像空间分辨率之间存在权衡。超分辨率常被提出作为兼顾两者的方法。在本研究中，我们探讨了卫星图像时间序列的多图像超分辨率，即如何利用在不同日期采集的同一区域的多幅图像来重建更高分辨率的观测结果。特别是，我们扩展了先进的深度单图像和多图像超分辨率算法（如SRDiff和HighRes-net），以处理不规则采样的Sentinel-2时间序列。我们引入了BreizhSR，这是一个用于Sentinel-2时间序列4倍超分辨率的新数据集，使用了法国布列塔尼地区的极高分辨率SPOT-6图像。我们表明，使用多幅图像显著提升了超分辨率性能，且精心设计的时间位置编码使我们能够对序列的不同时间执行超分辨率。此外，我们观察到重建HR图像的光谱保真度与感知质量之间存在权衡，这引发了对地球观测数据超分辨率未来方向的思考。
英文摘要：
Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get the best of both worlds. In this work, we investigate multi-image super-resolution of satellite image time series, i.e. how multiple images of the same area acquired at different dates can help reconstruct a higher resolution observation. In particular, we extend state-of-the-art deep single and multi-image super-resolution algorithms, such as SRDiff and HighRes-net, to deal with irregularly sampled Sentinel-2 time series. We introduce BreizhSR, a new dataset for 4x super-resolution of Sentinel-2 time series using very high-resolution SPOT-6 imagery of Brittany, a French region. We show that using multiple images significantly improves super-resolution performance, and that a well-designed temporal positional encoding allows us to perform super-resolution for different times of the series. In addition, we observe a trade-off between spectral fidelity and perceptual quality of the reconstructed HR images, questioning future directions for super-resolution of Earth Observation data.
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