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.

翻译：多光谱影像（MSI）中的云覆盖对早期作物制图构成了重大挑战，因为它会导致光谱信息的缺失或损坏。不受云干扰影响的合成孔径雷达（SAR）数据提供了补充解决方案，但缺乏用于精确作物制图的足够光谱细节。为解决这一问题，我们提出了一种新颖框架——基于视觉变换器（ViT）的时间序列MSI图像重构方法，通过利用MSI的时间相干性以及注意机制中SAR的补充信息，重构云覆盖区域的MSI数据。使用严格的图像重构评估指标进行的全面实验表明，时间序列ViT框架显著优于采用非时间序列MSI与SAR组合或未结合SAR的时间序列MSI的基线方法，有效增强了云覆盖区域的MSI图像重构效果。