用多模态卫星数据生成任意时间点的光学影像，实现云遮区域补全与未来预测。
Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models
- 融合哨兵1号SAR与哨兵2号光学数据，联合建模时序特征。
- 在稀疏且时间错位的数据上实现有效补全与未来图像生成。
- 关注生成结果的不确定性，适合遥感动态监测与决策支持场景。
光学卫星时间序列广泛应用于农业、气候监测和地表分析等领域。然而，云层和条带边缘导致时间维度采样不规则，限制了连续监测。现有方法虽能填补观测时间段内的缺失数据，但无法预测未来观测。本文提出一种概率深度学习框架，通过联合利用哨兵2号光学数据与哨兵1号SAR数据，生成任意过去或未来日期的光学影像，实现时间序列的稠密化与预报。该方法特别关注生成图像的不确定性。实验表明，在稀疏且时间错位的数据上，该方法可有效完成数据补全与未来预测。
原文摘要 · Abstract (English)
Optical satellite image time series are extensively used in many Earth observation applications, including agriculture, climate monitoring, and land surface analysis. However, clouds and swath edges result in irregular sampling along the temporal dimension, limiting continuous monitoring. To address this issue, a growing body of work has focused on temporal densification and reconstruction of satellite image time series, with the objective of filling missing or cloud-contaminated observations within the temporal extent of the available data. While these approaches improve temporal continuity, they are inherently restricted to the reconstruction of the gaps within the observed time periods, and do not address the prediction of future observations. This work proposes a probabilistic deep learning framework for the densification and forecasting of Sentinel-2 time series by generating optical images at arbitrary past or future dates. The approach leverages multimodal satellite data by jointly exploiting Sentinel-2 optical and Sentinel-1 SAR observations. Unlike most existing works, we propose to focus on the uncertainty of the generated images. Experimental results demonstrate effective densification and forecasting, on sparse and temporally misaligned time series.
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。