{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:42:58Z","timestamp":1781797378111,"version":"3.54.5"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Natural Science Fund of China","doi-asserted-by":"publisher","award":["62221002"],"award-info":[{"award-number":["62221002"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Natural Science Fund of China","doi-asserted-by":"publisher","award":["62171183"],"award-info":[{"award-number":["62171183"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Natural Science Fund of China","doi-asserted-by":"publisher","award":["U24A20327"],"award-info":[{"award-number":["U24A20327"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JJ20017"],"award-info":[{"award-number":["2022JJ20017"]}],"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":[[2025]]},"DOI":"10.1109\/tgrs.2024.3521586","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T19:17:13Z","timestamp":1734981433000},"page":"1-11","source":"Crossref","is-referenced-by-count":9,"title":["Hierarchical Augmentation and Region-Aware Contrastive Learning for Semi-Supervised Semantic Segmentation of Remote Sensing Images"],"prefix":"10.1109","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6016-1936","authenticated-orcid":false,"given":"Yuan","family":"Luo","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7029-8784","authenticated-orcid":false,"given":"Bin","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0585-9848","authenticated-orcid":false,"given":"Shutao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7986-6056","authenticated-orcid":false,"given":"Yulong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Robotics, Hunan University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref7","article-title":"Semi-supervised semantic segmentation needs strong, varied perturbations","author":"French","year":"2019","journal-title":"arXiv:1906.01916"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01092"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02270"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3272552"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3220755"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3102026"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3376352"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3277203"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.223"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3134277"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2960224"},{"key":"ref20","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":"ref21","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Xie"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3313619"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3239042"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3330490"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3318788"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3332490"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01598"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref30","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016","journal-title":"arXiv:1610.02242"},{"key":"ref31","first-page":"1195","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"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref34","first-page":"1","article-title":"Learning representations by maximizing mutual information across views","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Bachman"},{"key":"ref35","first-page":"4182","article-title":"Data-efficient image recognition with contrastive predictive coding","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Henaff"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01641"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01045"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00718"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref43","first-page":"1","article-title":"Adversarial learning for semi-supervised semantic segmentation","volume-title":"Proc. 29th Brit. Mach. Vis. Conf. (BMVC)","author":"Hung"},{"key":"ref44","article-title":"LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation","author":"Wang","year":"2021","journal-title":"arXiv:2110.08733"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10807682\/10812811.pdf?arnumber=10812811","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T06:09:18Z","timestamp":1736402958000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10812811\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3521586","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}