引用
Deshpande, A., Lu, J., Yeh, M.-C., Chong, M. J. and Forsyth, D., 2017. Learning diverse image colorization. In: Proc. CVPR, Honolulu, HI, USA, pp. 6837–6845.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P. et al., 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote sensing of Environment 120, pp. 25–36.
European Space Agency, 2015. Sentinels: Space for Coper- nicus. http://esamultimedia.esa.int/multimedia/ publications/sentinels-family/. [Online].
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, pp. 18–27.
Grohnfeldt, C., Schmitt, M. and Zhu, X., 2018. A conditional generative adversarial network to fuse SAR and multispectral op- tical data for cloud removal from Sentinel-2 images. In: Proc. IGARSS, Valencia, Spain. in press.
Hughes, L. H., Schmitt, M., Mou, L., Wang, Y. and Zhu, X. X., 2018. Identifying corresponding patches in SAR and optical im- ages with a pseudo-siamese CNN. IEEE Geoscience and Remote Sensing Letters 15(5), pp. 784–788.
Isola, P., Zhu, J.-Y., Zhou, T. and Efros, A. A., 2017. Image- to-image translation with conditional adversarial networks. In: Proc. CVPR, Honolulu, HI, USA, pp. 1125–1134.
Ley, A., d’Hondt, O., Valade, S., Ha ̈nsch, R. and Hellwich, O., 2018. Exploiting GAN-based SAR to optical image transcoding for improved classification via deep learning. In: Proc. EUSAR, Aachen, Germany, pp. 396–401.
Marmanis, D., Yao, W., Adam, F.Datcu, M., Reinartz, P., Schindler, K., Wegner, J. D. and Stilla, U., 2017. Artificial gen- eration of big data for improving image classification: a genera- tive adversarial network approach on SAR data. In: Proc. BiDS, Toulouse, France, pp. 293–296.
Merkle, N., Auer, S., Mu ̈ller, R. and Reinartz, P., 2018. Explor- ing the potential of conditional adversarial networks for optical and SAR image matching. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. in press.
Merkle, N., Wenjie, L., Auer, S., Mu ̈ller, R. and Urtasun, R., 2017. Exploiting deep matching and SAR data for the geo- localization accuracy improvement of optical satellite images. Remote Sensing 9(9), pp. 586–603.
Schmitt, M. and Zhu, X., 2016. Data fusion and remote sensing – an ever-growing relationship. IEEE Geosci. Remote Sens. Mag. 4(4), pp. 6–23.
Schmitt, M., Hughes, L. H., Ko ̈rner, M. and Zhu, X. X., 2018. Colorizing Sentinel-1 SAR images using a variational autoen- coder conditioned on Sentinel-2 imagery. In: Int. Arch. Pho- togramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-2, pp. 1045– 1051.
Schubert, A., Small, D., Miranda, N., Geudtner, D. and Meier, E., 2015. Sentinel-1a product geolocation accuracy: Commissioning phase results. Remote Sensing 7(7), pp. 9431–9449.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M. et al., 2012. GMES Sentinel-1 mission. Remote Sensing of Environment 120, pp. 9–24.
Wang, P. and Patel, V. M., 2018. Generating high quality visible images from SAR images using CNNs. arXiv:1802.10036.
Wang, Y. and Zhu, X. X., 2018. The SARptical dataset for joint analysis of SAR and optical image in dense urban area. arXiv:1801.07532.
Zhang, L., Zhang, L. and Du, B., 2016. Deep learning for remote sensing data. IEEE Geoscience and Remote Sensing Magazine 4(2), pp. 22–40.
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F. and Fraundorfer, F., 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5(4), pp. 8–36.