Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913336560451584 |
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| author | Godwin, Denys Li, Hanxi Cecil, Michael Alemohammad, Hamed |
| author_facet | Godwin, Denys Li, Hanxi Cecil, Michael Alemohammad, Hamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model Godwin, Denys Li, Hanxi Cecil, Michael Alemohammad, Hamed Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2404.19609 |