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Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention.
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Remote sensing for biodiversity science and conservation
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Sentinel-2: Esa’s optical high-resolution mission for gmes operational services
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Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Urban change detection for multispectral earth observation using convolutional neural networks
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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
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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In-domain representation learning for remote sensing
Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, and Neil Houlsby · 2019
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Bigearthnet: A large-scale benchmark archive for remote sensing image understanding
Gencer Sumbul, Marcela Charfuelan, Begüm Demir, and Volker Markl · 2019
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Self-supervised remote sensing images change detection at pixel-level
Yuxing Chen and Lorenzo Bruzzone · 2021
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Seed: Self-supervised distillation for visual representation
Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, Yezhou Yang, and Zicheng Liu · 2021
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A historical and future impact assessment of mining activities on surface biophysical characteristics change: A remote sensing-based approach
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Review on convolutional neural networks (cnn) in vegetation remote sensing
T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Can temporal information help with contrastive self-supervised learning?
Yutong Bai, Haoqi Fan, Ishan Misra, Ganesh Venkatesh, Yongyi Lu, Yuyin Zhou, Qihang Yu, Vikas Chandra, and Alan Yuille · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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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
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Bigearthnet dataset with a new class-nomenclature for remote sensing image understanding
Gencer Sumbul, Jian Kang, Tristan Kreuziger, Filipe Marcelino, Hugo Costa, Pedro Benevides, Mario Caetano, and Begüm Demir · 2020
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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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Efficient deep learning models for land cover image classification
Ioannis Papoutsis, Nikolaos-Ioannis Bountos, Angelos Zavras, Dimitrios Michail, and Christos Tryfonopoulos · 2021
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Self-supervision. remote sensing and abstraction: Representation learning across 3 million locations
Sachith Seneviratne, Kerry A Nice, Jasper S Wijnands, Mark Stevenson, and Jason Thompson · 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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The color out of space: learning self-supervised representations for earth observation imagery
Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Marco Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, Annamaria Conte, and Simone Calderara · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, and Nicolas Ballas · 2022
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Self-supervised vision transformers for joint sar-optical representation learning
Yi Wang, Conrad M Albrecht, and Xiao Xiang Zhu · 2022
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Self-distilled self-supervised representation learning
Jiho Jang, Seonhoon Kim, Kiyoon Yoo, Chaerin Kong, Jangho Kim, and Nojun Kwak · 2023
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Multi-mode online knowledge distillation for self-supervised visual representation learning
Kaiyou Song, Jin Xie, Shan Zhang, and Zimeng Luo · 2023
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Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works
Chao Tao, Ji Qi, Mingning Guo, Qing Zhu, and Haifeng Li · 2023
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Ssl4eo-s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets]
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M Albrecht, and Xiao Xiang Zhu · 2023
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