CLOUDTRAN: CLOUD REMOVAL FROM MULTITEMPORAL 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 multitemporal images in a given region of interest together with the corresponding cloud masks, and delivers a cloud-free image for a specific day of the year. We propose the c...

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Bibliographic Details
Main Authors: Christopoulos, D., Ntouskos, V., Karantzalos, K.
Format: Conference Proceeding
Language:English
Subjects:
Archives & records
Clouds
Coders
Encoders-Decoders
Image quality
Image resolution
Methods
Neural networks
Quality assessment
Remote sensing
Removal
Resolution
Satellite imagery
Satellites
Spaceborne remote sensing
Transformers
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Description
Summary:We present a method for cloud-removal from satellite images using axial transformer networks. The method considers a set of multitemporal images in a given region of interest together with the corresponding cloud masks, and delivers 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 two Sentinel-2 datasets, showing significant improvements across multiple standard metrics used for image quality assessment.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIII-B2-2022-1125-2022