CloudTran++: Improved Cloud Removal from Multi-Temporal Satellite Images Using Axial Transformer Networks
We present a method for cloud removal from satellite images using axial transformer networks. The method considers a set of multi-temporal images in a given region of interest, together with the corresponding cloud masks, and produces a cloud-free image for a specific day of the year. We propose the...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/1/86 |
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| Summary: | We present a method for cloud removal from satellite images using axial transformer networks. The method considers a set of multi-temporal images in a given region of interest, together with the corresponding cloud masks, and produces a cloud-free image for a specific day of the year. We propose the combination of an encoder-decoder model employing axial attention layers for the estimation of the low-resolution cloud-free image, together with a fully parallel upsampler that reconstructs the image at full resolution. The method is compared with various baselines and state-of-the-art methods on Sentinel-2 datasets of different coverage, showing significant improvements across multiple standard metrics used for image quality assessment. |
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| ISSN: | 2072-4292 |