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While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing.
Sentinel-2: ESA’s optical high-resolution mission for GMES operational services
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P. et al., 2012 · 2012
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GMES Sentinel-1 mission
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M. et al., 2012 · 2012
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Sentinels: Space for Copernicus
European Space Agency, 2015 · 2015
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Sentinel-1a product geolocation accuracy: Commissioning phase results
Schubert, A., Small, D., Miranda, N., Geudtner, D. and Meier, E., 2015 · 2015
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Data fusion and remote sensing – an ever-growing relationship
Schmitt, M. and Zhu, X., 2016 · 2016
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Deep learning for remote sensing data
Zhang, L., Zhang, L. and Du, B., 2016 · 2016
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Learning diverse image colorization
Deshpande, A., Lu, J., Yeh, M.-C., Chong, M. J. and Forsyth, D., 2017 · 2017
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Google earth engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017 · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T. and Efros, A. A., 2017 · 2017
Cited alongside, same era.
Artificial generation of big data for improving image classification: a generative adversarial network approach on SAR data
Marmanis, D., Yao, W., Adam, F., Datcu, M., Reinartz, P., Schindler, K., Wegner, J. D. and Stilla, U., 2017 · 2017
Cited alongside, same era.
Exploiting deep matching and SAR data for the geo-localization accuracy improvement of optical satellite images
Merkle, N., Wenjie, L., Auer, S., Müller, R. and Urtasun, R., 2017 · 2017
Cited alongside, same era.
Deep learning in remote sensing: A comprehensive review and list of resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F. and Fraundorfer, F., 2017 · 2017
Identifying corresponding patches in SAR and optical images with a pseudo-siamese CNN
Hughes, L. H., Schmitt, M., Mou, L., Wang, Y. and Zhu, X. X., 2018 · 2018
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Exploiting GAN-based SAR to optical image transcoding for improved classification via deep learning
Ley, A., d’Hondt, O., Valade, S., Hänsch, R. and Hellwich, O., 2018 · 2018
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Exploring the potential of conditional adversarial networks for optical and SAR image matching
Merkle, N., Auer, S., Müller, R. and Reinartz, P., 2018 · 2018
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Colorizing Sentinel-1 SAR images using a variational autoencoder conditioned on Sentinel-2 imagery
Schmitt, M., Hughes, L. H., Körner, M. and Zhu, X. X., 2018 · 2018
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Generating high quality visible images from SAR images using CNNs
Wang, P. and Patel, V. M., 2018 · 2018
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Cited alongside, same era.
A conditional generative adversarial network to fuse SAR and multispectral optical data for cloud removal from Sentinel-2 images
Grohnfeldt, C., Schmitt, M. and Zhu, X., 2018 · 2018
Cited alongside, same era.
Wang, Y. and Zhu, X. X., 2018 · 2018
Closest in time.