Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model

arXiv:2404.19609v1 Announce Type: new
Abstract: 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 …

abstract analysis applications arxiv cloud cs.cv data data analysis eess.iv foundation foundational foundation model gap generative imputation issue performance pixels satellite series tasks through time series transformer type vision vit