Self-Supervised Learning in Remote Sensing

被引：144
作者：
Wang, Yi [1 ,2 ]
Albrecht, Conrad M. [3 ,4 ,5 ,6 ,16 ]
Ait Ali Braham, Nassim [1 ,2 ,7 ,8 ,9 ,10 ]
Mou, Lichao [11 ,12 ,13 ,14 ,15 ]
Zhu, Xiao Xiang [8 ,17 ,18 ,19 ,20 ,21 ,22 ,23 ,24 ,25 ,26 ,27 ,28 ,29 ,30 ,31 ]
机构：
[1] German Aerosp Ctr, D-82234 Wessling, Germany
[2] Tech Univ Munich, D-80333 Munich, Germany
[3] CERN, Meyrin, Switzerland
[4] Dresden Max Planck Inst Phys Complex Syst, Dresden, Germany
[5] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany
[6] DLR, D-82234 Wessling, Germany
[7] German Aerosp Ctr, Remote Sensing Technol Inst, D-82234 Wessling, Germany
[8] Tech Univ Munich, Data Sci Earth Observat, D-80333 Munich, Germany
[9] LIRIS CNRS Lab, Lyon, France
[10] PSL Res Univ, LAM SADE CNRS Lab, Paris, France
[11] TUM, Munich AI Future Lab AI4EO, D-80333 Munich, Germany
[12] German Aerosp Ctr DLR, Visual Learning & Reasoning Team, Dept EO Data Sci, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany
[13] DLR IMF, Wessling, Germany
[14] Univ Freiburg, Comp Vis Grp, Freiburg, Germany
[15] Univ Cambridge, Cambridge Image Anal Grp, Cambridge, England
[16] IBM TJ Watson Res Ctr, Yorktown Hts, NY USA
[17] German Aerosp Ctr DLR, EO Data Sci Dept, Remote Sensing Technol Inst, D-82234 Wessling, Germany
[18] Munich Data Sci Res Sch, Munich, Germany
[19] Helmholtz Artificial Intelligence Res Field Aeron, Wessling, Germany
[20] Artificial Intelligence Earth Observat Reason Rea, Int Future Artificial Intelligence AI Lab AI4EO, D-85521 Ottobrunn, Germany
[21] TUM, Munich Data Sci Inst, D-80333 Munich, Germany
[22] Italian Natl Res Council CNR IREA, Naples, Italy
[23] Fudan Univ, Shanghai, Peoples R China
[24] Univ Tokyo, Tokyo, Japan
[25] Univ Calif Los Angeles, Los Angeles, CA USA
[26] European Space Agcy, Phi Lab, Frascati, Italy
[27] Berlin Brandenburg Acad Sci & Humanities, Young Acad Junge Akad Junges Kolleg, Berlin, Germany
[28] German Natl Acad Sci Leopoldina, Halle, Saale, Germany
[29] Bavarian Acad Sci & Humanities, Munich, Germany
[30] German Res Ctr Geosci GFZ, Potsdam, Germany
[31] Potsdam Inst Climate Impact Res PIK, Potsdam, Germany
来源：
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE | 2022年 / 10卷 / 04期
关键词：
Self-supervised learning; Learning systems; LAND-COVER; MANIFOLD ALIGNMENT; FEATURE-EXTRACTION; REPRESENTATIONS; NETWORK; CLASSIFICATION; IMAGES; SAR; AUTOENCODERS; SEGMENTATION;
D O I：
10.1109/MGRS.2022.3198244
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains. © 2013 IEEE.
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收藏
页码：213 / 247
页数：35
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