用视觉变压器融合时序影像与雷达数据，修复云遮挡的多光谱图像。
Vision Transformer-Based Time-Series Image Reconstruction for Cloud-Filling Applications
- 基于视觉变压器，利用时序影像和雷达数据的注意力机制重建云区影像
- 在云覆盖区域的重建精度显著优于仅用影像或仅用雷达的基线方法
- 适合需要高精度早期作物监测的遥感应用
多光谱影像（MSI）中的云覆盖给早期作物制图带来重大挑战，导致光谱信息缺失或损坏。合成孔径雷达（SAR）数据不受云干扰，可作为补充，但缺乏足够的光谱细节以实现精确作物制图。为此，我们提出一种新框架——基于视觉变压器（ViT）的时序多光谱影像重建，通过利用多光谱影像的时间一致性及来自SAR的互补信息，借助注意力机制重建云覆盖区域的影像。综合实验表明，该时序ViT框架在严格重建评估指标下显著优于仅使用非时序的MSI与SAR，或仅使用时序MSI但无SAR的基线方法，有效提升了云覆盖区域的多光谱影像重建效果。
原文摘要 · Abstract (English)
Cloud cover in multispectral imagery (MSI) poses significant challenges for early season crop mapping, as it leads to missing or corrupted spectral information. Synthetic aperture radar (SAR) data, which is not affected by cloud interference, offers a complementary solution, but lack sufficient spectral detail for precise crop mapping. To address this, we propose a novel framework, Time-series MSI Image Reconstruction using Vision Transformer (ViT), to reconstruct MSI data in cloud-covered regions by leveraging the temporal coherence of MSI and the complementary information from SAR from the attention mechanism. Comprehensive experiments, using rigorous reconstruction evaluation metrics, demonstrate that Time-series ViT framework significantly outperforms baselines that use non-time-series MSI and SAR or time-series MSI without SAR, effectively enhancing MSI image reconstruction in cloud-covered regions.
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