Sentinel-2 Cloud Removal Considering Ground Changes by Fusing Multitemporal SAR and Optical Images

Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-10, Vol.13 (19), p.3998
Main Authors: Gao, Jianhao, Yi, Yang, Wei, Tang, Zhang, Guanhao
Format: Article
Language:English
Subjects:
Artificial neural networks
cloud removal
Datasets
Deep learning
Image reconstruction
Methods
multitemporal
Neural networks
Radar imaging
Real time
Remote sensing
SAR
Satellite imagery
Satellites
Sentinel-2
Simulation
Synthetic aperture radar
Training
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Summary:Publicly available optical remote sensing images from platforms such as Sentinel-2 satellites contribute much to the Earth observation and research tasks. However, information loss caused by clouds largely decreases the availability of usable optical images so reconstructing the missing information is important. Existing reconstruction methods can hardly reflect the real-time information because they mainly make use of multitemporal optical images as reference. To capture the real-time information in the cloud removal process, Synthetic Aperture Radar (SAR) images can serve as the reference images due to the cloud penetrability of SAR imaging. Nevertheless, large datasets are necessary because existing SAR-based cloud removal methods depend on network training. In this paper, we integrate the merits of multitemporal optical images and SAR images to the cloud removal process, the results of which can reflect the ground information change, in a simple convolution neural network. Although the proposed method is based on deep neural network, it can directly operate on the target image without training datasets. We conduct several simulation and real data experiments of cloud removal in Sentinel-2 images with multitemporal Sentinel-1 SAR images and Sentinel-2 optical images. Experiment results show that the proposed method outperforms those state-of-the-art multitemporal-based methods and overcomes the constraint of datasets of those SAR-based methods.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13193998