Multisensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
نویسندگان
چکیده
The majority of optical observations acquired via spaceborne Earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are, oftentimes, confined to narrowly defined regions interest, raising the question whether an approach can generalize a diverse set at variable cloud coverage or in different and seasons. We target challenge generalization curating large novel data for training new removal approaches evaluate two recently proposed performance metrics image quality diversity. Our first publically available contain global sample coregistered radar observations, cloudy cloud-free. Based observation that varies widely between clear skies absolute coverage, we propose model deal with either extreme its our set. Finally, demonstrate superiority models real over synthetic data, underlining need carefully curated observations. To facilitate future research, made online.
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2021
ISSN: ['0196-2892', '1558-0644']
DOI: https://doi.org/10.1109/tgrs.2020.3024744