{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T18:00:27Z","timestamp":1774634427294,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["62201550"],"award-info":[{"award-number":["62201550"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Key Deployment Program of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["KGFZD-145-23-18"],"award-info":[{"award-number":["KGFZD-145-23-18"]}],"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":[[2024]]},"DOI":"10.1109\/tgrs.2024.3407142","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T17:48:32Z","timestamp":1717091312000},"page":"1-14","source":"Crossref","is-referenced-by-count":13,"title":["Dynamic and Adaptive Self-Training for Semi-Supervised Remote Sensing Image Semantic Segmentation"],"prefix":"10.1109","volume":"62","author":[{"given":"Jidong","family":"Jin","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4612-508X","authenticated-orcid":false,"given":"Wanxuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8138-1372","authenticated-orcid":false,"given":"Hongfeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7681-7020","authenticated-orcid":false,"given":"Xuee","family":"Rong","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0038-9816","authenticated-orcid":false,"given":"Xian","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Yirong","family":"Wu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2021.3136100"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2022.3145854"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3152587"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-019-2791-7"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.02.009"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2979552"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3053062"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/rs10050743"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/s18113774"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2022.06.008"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3175191"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3144165"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/rs14040879"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3134277"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3119286"},{"key":"ref19","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen"},{"issue":"2","key":"ref20","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop Challenges Represent. Learn.","volume":"3","author":"Lee"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3220755"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/rs12010159"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/rs12213603"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3102026"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2917952"},{"key":"ref27","article-title":"ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring","author":"Berthelot","year":"2019","journal-title":"arXiv:1911.09785"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2020.114809"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"ref30","first-page":"4055","article-title":"Image transformer","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Parmar"},{"key":"ref31","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3201688"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/rs13183585"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref35","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume-title":"Proc. Adv. Neural Inf. Process. Sys. (NIPS)","volume":"34","author":"Xie"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2960224"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108777"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3144036"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00421"},{"key":"ref41","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Sohn"},{"key":"ref42","article-title":"FreeMatch: Self-adaptive thresholding for semi-supervised learning","author":"Wang","year":"2022","journal-title":"arXiv:2205.07246"},{"key":"ref43","article-title":"SoftMatch: Addressing the quantity-quality trade-off in semi-supervised learning","author":"Chen","year":"2023","journal-title":"arXiv:2301.10921"},{"key":"ref44","article-title":"SelfMatch: Combining contrastive self-supervision and consistency for semi-supervised learning","author":"Kim","year":"2021","journal-title":"arXiv:2101.06480"},{"key":"ref45","article-title":"Generative adversarial networks","author":"Goodfellow","year":"2014"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.5194\/isprsannals-i-3-293-2012"},{"key":"ref47","first-page":"28","article-title":"iSAID: A large-scale dataset for instance segmentation in aerial images","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. Workshops","author":"Zamir"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.5244\/C.27.32"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00747"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10354519\/10542113.pdf?arnumber=10542113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T17:25:15Z","timestamp":1726853115000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10542113\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3407142","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}