{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:38:13Z","timestamp":1791916693145,"version":"4.3.4"},"reference-count":75,"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:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"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"}]},{"DOI":"10.13039\/501100023371","name":"Helmholtz Artificial Intelligence Cooperation Unit","doi-asserted-by":"publisher","award":["ZT-I-PF-5-01"],"award-info":[{"award-number":["ZT-I-PF-5-01"]}],"id":[{"id":"10.13039\/501100023371","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.2024.3373908","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:07:54Z","timestamp":1709665674000},"page":"1-17","source":"Crossref","is-referenced-by-count":20,"title":["AIO2: Online Correction of Object Labels for Deep Learning With Incomplete Annotation in Remote Sensing Image Segmentation"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9172-3586","authenticated-orcid":false,"given":"Chenying","family":"Liu","sequence":"first","affiliation":[{"name":"Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2422-7289","authenticated-orcid":false,"given":"Conrad M.","family":"Albrecht","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, German Aerospace Center (DLR), Weßling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3096-6610","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyu","family":"Li","sequence":"additional","affiliation":[{"name":"Chair of Data Science in Earth Observation, Technical University of Munich (TUM), 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 (TUM), Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.2994107"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9672060"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01307-4"},{"key":"ref5","volume-title":"ESA WorldCover 10 m 2020 v100","author":"Zanaga","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.2964708"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020164"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2019.8898003"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3000314"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3128539"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2765364"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.5194\/isprs-annals-V-2-2022-275-2022"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3246102"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6971"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3281503"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2022.04.012"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3068280"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2022.113371"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00263"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10562-9"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7080289"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2021.2023158"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-39698-6"},{"key":"ref26","first-page":"1","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":"ref27","first-page":"1","article-title":"Learning with noisy labels revisited: A study using real-world human annotations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wei"},{"key":"ref28","article-title":"Training convolutional networks with noisy labels","author":"Sukhbaatar","year":"2014","journal-title":"arXiv:1406.2080"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472164"},{"key":"ref30","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Goldberger"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref32","first-page":"17044","article-title":"Identifying mislabeled data using the area under the margin ranking","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Pleiss"},{"key":"ref33","first-page":"1","article-title":"Decoupling \u2018when to update\u2019 from \u2018how to update","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Malach"},{"key":"ref34","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. ICML","author":"Jiang"},{"key":"ref35","first-page":"1","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Han"},{"key":"ref36","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref37","first-page":"1","article-title":"Dividemix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548053"},{"key":"ref39","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. NIPS","volume":"33","author":"Liu"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref41","first-page":"1","article-title":"Training deep neural networks on noisy labels with bootstrapping","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Reed"},{"key":"ref42","first-page":"1","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Zhang"},{"key":"ref43","article-title":"Curriculum loss: Robust learning and generalization against label corruption","author":"Lyu","year":"2019","journal-title":"arXiv:1905.10045"},{"key":"ref44","first-page":"6226","article-title":"Peer loss functions: Learning from noisy labels without knowing noise rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2223460"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.2112\/SI102-019.1"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.2965558"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.102863"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2989241"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3025174"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3056661"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3042607"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3226371"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3209992"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3293776"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2612821"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2719738"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2021.1973685"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3023112"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2963065"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412054"},{"key":"ref64","first-page":"1","article-title":"Label super-resolution networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Malkin"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3390\/rs13091689"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi9070427"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.3390\/rs14041000"},{"key":"ref68","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Laine"},{"key":"ref69","first-page":"233","article-title":"A closer look at memorization in deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arpit"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.1994.576361"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"ref72","volume-title":"Machine learning for aerial image labeling","author":"Mnih","year":"2013"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS46834.2022.9884076"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref75","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","volume":"97","author":"Tan"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10354519\/10460569.pdf?arnumber=10460569","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T21:19:22Z","timestamp":1711487962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10460569\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":75,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3373908","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}