[13] Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, and Xiao Xiang Zhu. SSL4EO-S12: A large-scale multi-modal, multi-temporal dataset for self-supervised learning in Earth observation. arXiv preprint arXiv:2211.07044, 2022.
[14] Di Wang, Jing Zhang, Bo Du, Gui-Song Xia, and Dacheng Tao. An empirical study of remote sensing pretraining. IEEE Transactions on Geoscience and Remote Sensing, 2022.
[15] Yi Wang, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu. Self-supervised learning in remote sensing: A review. arXiv preprint arXiv:2206.13188, 2022.
[16] Paul Berg, Minh-Tan Pham, and Nicolas Courty. Self-supervised learning for scene classification in remote sensing: Current state of the art and perspectives. Remote Sensing, 14(16):3995, 2022.
[17] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with Momentum. Contrastive learning. arXiv preprint arXiv:2003.04297, 2020.
[18] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33:9912-9924, 2020.
[19] Xinlei Chen and Kaiming He. Exploring simple Siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15750-15758, 2021.
[20] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stephane Deny. Barlow Twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310-12320. PMLR, 2021.
[21] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, pages 1597-1607. PMLR, 2020.
[22] Jean-Bastien Grill, Florian Strub, Florent Altché, Coretin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap Your Own Latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems, 33:21271-21284, 2020.
[23] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255. IEEE, 2009.
[24] Yuxing Chen and Lorenzo Bruzzone. Self-supervised SAR-optical data fusion of Sentinel-1/-2 images. IEEE Transactions on Geoscience and Remote Sensing, 60:1-11, 2021.
[25] Marrit Leenstra, Diego Marcos, Francesca Bovolo, and Devis Tuia. Self-supervised pretraining enhances change detection in Sentinel-2 imagery. In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10-15, 2021, Proceedings, Part VII, pages 578-590. Springer, 2021.
[26] Jamie Tolan, Hung-I Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, et al. Sub-meter resolution canopy height maps using self-supervised learning and a vision transformer trained on Aerial and GEDI Lidar. arXiv preprint arXiv:2304.07213, 2023.
[27] Jules Bourcier, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier, Karteek Alahari, and Jocelyn Chanussot. Self-supervised pretraining on satellite imagery: A case study on label-efficient vehicle detection. arXiv preprint arXiv:2210.11815, 2022.
[28] Bo Peng, Qunying Huang, Jamp Vongkusolkit, Song Gao, Daniel B. Wright, Zheng N. Fang, and Yi Qiang. Urban flood mapping with bitemporal multispectral imagery via a self-supervised learning framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14:2001-2016, 2020.