{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:49:27Z","timestamp":1777567767483,"version":"3.51.4"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62125102"],"award-info":[{"award-number":["62125102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tgrs.2022.3202499","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T20:40:22Z","timestamp":1661805622000},"page":"1-20","source":"Crossref","is-referenced-by-count":15,"title":["Geographical Supervision Correction for Remote Sensing Representation Learning"],"prefix":"10.1109","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3889-2775","authenticated-orcid":false,"given":"Wenyuan","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, the State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, and the Image Processing Center, School of Astronautics,, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0483-1306","authenticated-orcid":false,"given":"Keyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, the State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, and the Image Processing Center, School of Astronautics,, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3172","authenticated-orcid":false,"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, the State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, and the Image Processing Center, School of Astronautics,, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3005403"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3016820"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2572736"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.11.023"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.09.014"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.05.005"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2988265"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.02.017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.04.014"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2964675"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/rs13214441"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.167"},{"key":"ref13","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Gidaris"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00289"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00265"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_35"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.96"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.76"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref22","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref23","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Grill"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref26","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04297"},{"key":"ref27","first-page":"21798","article-title":"Hard negative mixing for contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Kalantidis"},{"key":"ref28","first-page":"8765","article-title":"Debiased contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chuang"},{"key":"ref29","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Caron"},{"key":"ref30","first-page":"12310","article-title":"Barlow Twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zbontar"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00828"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00221"},{"key":"ref33","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Radford"},{"key":"ref34","article-title":"EfficientCLIP: Efficient cross-modal pre-training by ensemble confident learning and language modeling","author":"Wang","year":"2021","journal-title":"arXiv:2109.04699"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3007029"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01002"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00928"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.2994107"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3115569"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2014.09.002"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.5194\/essd-13-2753-2021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2012.748992"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00654"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref45","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00519"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869829"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2475299"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.5194\/isprsannals-i-3-293-2012"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.01.023"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2773199"},{"key":"ref53","article-title":"Objects as points","author":"Zhou","year":"2019","journal-title":"arXiv:1904.07850"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/9633014\/09869651.pdf?arnumber=9869651","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T06:03:49Z","timestamp":1709359429000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9869651\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2022.3202499","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}