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Self-supervised methods have shown tremendous success in the field of computer vision, including applications in remote sensing and medical imaging.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Integration of optical and synthetic aperture radar (sar) imagery for delivering operational annual crop inventories
Heather McNairn, Catherine Champagne, Jiali Shang, Delmar Holmstrom, and Gordon Reichert · 2009
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Sentinel-2: Esa’s optical high-resolution mission for gmes operational services
Matthias Drusch, Umberto Del Bello, Sébastien Carlier, Olivier Colin, Veronica Fernandez, Ferran Gascon, Bianca Hoersch, Claudia Isola, Paolo Laberinti, Philippe Martimort, et al · 2012
Earlier work this paper cites.
Gmes sentinel-1 mission
Ramon Torres, Paul Snoeij, Dirk Geudtner, David Bibby, Malcolm Davidson, Evert Attema, Pierre Potin, BjÖrn Rommen, Nicolas Floury, Mike Brown, et al · 2012
Earlier work this paper cites.
Sea ice monitoring by synthetic aperture radar
Wolfgang Dierking · 2013
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
Earlier work this paper cites.
Backscatter analysis using multi-temporal and multi-frequency sar data in the context of flood mapping at river saale, germany
Sandro Martinis and Christoph Rieke · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Google earth engine: Planetary-scale geospatial analysis for everyone
Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore · 2017
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Earlier work this paper cites.
Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
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Convolutional neural network for remote-sensing scene classification: Transfer learning analysis
Rafael Pires de Lima and Kurt Marfurt · 2019
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Predicting economic development using geolocated wikipedia articles
Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon · 2019
Earlier work this paper cites.
Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data
N Anusha and B Bharathi · 2020
Cited alongside, same era.
Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1
Derrick Bonafilia, Beth Tellman, Tyler Anderson, and Erica Issenberg · 2020
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Cited alongside, same era.
Multi-modal self-supervised representation learning for earth observation
Pallavi Jain, Bianca Schoen-Phelan, and Robert Ross · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Later among the works it cites.
Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data
Oscar Manas, Alexandre Lacoste, Xavier Giró-i Nieto, David Vazquez, and Pau Rodriguez · 2021
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Synthetic aperture radar (sar): Principles and applications
Alberto Moreira · 2021
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Evaluating self and semi-supervised methods for remote sensing segmentation tasks
Chaitanya Patel, Shashank Sharma, and Varun Gulshan · 2021
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi · 2020
Cited alongside, same era.
Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases
Senthil Purushwalkam and Abhinav Gupta · 2020
Cited alongside, same era.
https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD
Sentinel-1 sar grd: C-band synthetic aperture radar ground range detected, log scaling · 2021
Cited alongside, same era.
https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2
Sentinel-2 msi: Multispectral instrument, level-1c · 2021
Cited alongside, same era.
Geography-aware self-supervised learning
Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Self-supervised multisensor change detection
Sudipan Saha, Patrick Ebel, and Xiao Xiang Zhu · 2021
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Self-supervised learning of remote sensing scene representations using contrastive multiview coding
Vladan Stojnic and Vladimir Risojevic · 2021
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Revisiting contrastive methods for unsupervised learning of visual representations
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc V Gool · 2021
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Unsupervised feature learning by cross-level instance-group discrimination
Xudong Wang, Ziwei Liu, and Stella X Yu · 2021
Later among the works it cites.
Geoclr: Georeference contrastive learning for efficient seafloor image interpretation
Takaki Yamada, Adam Prügel-Bennett, Stefan B Williams, Oscar Pizarro, and Blair Thornton · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Dynamic world, near real-time global 10 m land use land cover mapping
Christopher F Brown, Steven P Brumby, Brookie Guzder-Williams, Tanya Birch, Samantha Brooks Hyde, Joseph Mazzariello, Wanda Czerwinski, Valerie J Pasquarella, Robert Haertel, Simon Ilyushchenko, et al · 2022
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When does contrastive visual representation learning work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, and Serge Belongie · 2022
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Self-supervised learning for invariant representations from multi-spectral and sar images
Pallavi Jain, Bianca Schoen-Phelan, and Robert Ross · 2022
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