  Filling cloudy pixels in multispectral satellite imagery is essential for
accurate data analysis and downstream applications, especially for tasks which
require time series data. To address this issue, we compare the performance of
a foundational Vision Transformer (ViT) model with a baseline Conditional
Generative Adversarial Network (CGAN) model for missing value imputation in
time series of multispectral satellite imagery. We randomly mask time series of
satellite images using real-world cloud masks and train each model to
reconstruct the missing pixels. The ViT model is fine-tuned from a pretrained
model, while the CGAN is trained from scratch. Using quantitative evaluation
metrics such as structural similarity index and mean absolute error as well as
qualitative visual analysis, we assess imputation accuracy and contextual
preservation.
