The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
Open accessAbstract
Abstract The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance – GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40 → 20 m, respectively 360 → 60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD.
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Cited by
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
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Vivone, G., Dalla Mura, M., Garzelli, A., Restaino, R., Scarpa, G., Ulfarsson, M. O., Alparone, L., & Chanussot, J. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3019315 · Cited by 359
Metadata
- Authors
- Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 146
- Pages
- 305-319
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2794252992
- Cited by
- 371
- References
- 37
- FWCI
- 15.70
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- Swiss National Science Foundation (No. 200021_162998)
- Fundação para a Ciência e a Tecnologia (UID/EEA/50008/2013)
- Portuguese Ministry of Science, Technology and Higher Education (ERANETMED/0001/2014)
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- Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E., & Schindler, K. (2018). Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2018.09.018
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