{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T07:58:50Z","timestamp":1781078330197,"version":"3.54.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["ZH 498\/18-1"],"award-info":[{"award-number":["ZH 498\/18-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006360","name":"Bundesministerium f?r Wirtschaft und Energie","doi-asserted-by":"publisher","award":["50EE2201C"],"award-info":[{"award-number":["50EE2201C"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f?r Bildung und Forschung","doi-asserted-by":"publisher","award":["01DD20001"],"award-info":[{"award-number":["01DD20001"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tgrs.2023.3332490","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T19:13:22Z","timestamp":1701198802000},"page":"1-16","source":"Crossref","is-referenced-by-count":22,"title":["AdaptMatch: Adaptive Matching for Semisupervised Binary Segmentation of Remote Sensing Images"],"prefix":"10.1109","volume":"61","author":[{"given":"Wei","family":"Huang","sequence":"first","affiliation":[{"name":"Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilei","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Engineering and Design, Technical University of Munich (TUM), Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3953-585X","authenticated-orcid":false,"given":"Zhitong","family":"Xiong","sequence":"additional","affiliation":[{"name":"Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8107-9096","authenticated-orcid":false,"given":"Xiao Xiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS46834.2022.9883996"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102824"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2926397"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.103159"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3203314"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2849692"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3282935"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2023.3267879"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3268038"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3220845"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3233637"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2019.103740"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23786-8_11"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2020.106843"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/rs15010271"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3064606"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS46834.2022.9884017"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113448"},{"key":"ref19","article-title":"PseudoSeg: Designing pseudo labels for semantic segmentation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zou"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01876"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref25","first-page":"529","article-title":"Semi-supervised learning by entropy minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"17","author":"Grandvalet"},{"issue":"2","key":"ref26","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"3","author":"Lee"},{"key":"ref27","first-page":"5049","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Berthelot"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00421"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00685"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00973"},{"key":"ref34","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sohn"},{"key":"ref35","article-title":"SoftMatch: Addressing the quantity-quality trade-off in semi-supervised learning","author":"Chen","year":"2023","journal-title":"arXiv:2301.10921"},{"key":"ref36","first-page":"18408","article-title":"FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Zhang"},{"key":"ref37","article-title":"Revisiting weak-to-strong consistency in semi-supervised semantic segmentation","author":"Yang","year":"2022","journal-title":"arXiv:2208.09910"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2022.11.013"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3185795"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3243853"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3021098"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/rs12213603"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2022.3157032"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/jstars.2022.3203750"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref47","article-title":"Semi-supervised semantic segmentation needs strong, varied perturbations","author":"French","year":"2019","journal-title":"arXiv:1906.01916"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3247605"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3314452"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00155"},{"key":"ref51","article-title":"Revisiting consistency regularization for semi-supervised change detection in remote sensing images","author":"Bandara","year":"2022","journal-title":"arXiv:2204.08454"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3134277"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50835-1_22"},{"key":"ref54","article-title":"Machine learning for aerial image labeling","author":"Mnih","year":"2013"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.08.019"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00187"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref61","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10006360\/10329942.pdf?arnumber=10329942","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:12:11Z","timestamp":1710360731000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10329942\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3332490","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}