{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:20:14Z","timestamp":1763533214909,"version":"3.45.0"},"reference-count":56,"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022ZD0160805"],"award-info":[{"award-number":["2022ZD0160805"]}],"id":[{"id":"10.13039\/501100012166","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.2025.3624045","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T17:09:05Z","timestamp":1761066545000},"page":"1-17","source":"Crossref","is-referenced-by-count":0,"title":["Manifold-Aligned Consistency Contrastive Learning for Noise-Tolerant Hyperspectral Image Classification"],"prefix":"10.1109","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7175-1068","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"}]},{"given":"Junti","family":"Wang","sequence":"additional","affiliation":[{"name":"Rocket Force University of Engineering, Xi’an, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5900-6763","authenticated-orcid":false,"given":"Guanxiong","family":"He","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4814-1115","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9240-6726","authenticated-orcid":false,"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0871-6519","authenticated-orcid":false,"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, Shaanxi, China"}]},{"given":"Xuelong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), China Telecom, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3325444"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3219855"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3218284"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3368079"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3029523"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3254159"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111606"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/rs13245009"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106771"},{"key":"ref11","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"33","author":"Liu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01122"},{"key":"ref13","first-page":"1","article-title":"Robust early-learning: Hindering the memorization of noisy labels","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xia"},{"key":"ref14","first-page":"16377","article-title":"CrossSplit: Mitigating label noise memorization through data splitting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3057689"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2975936"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2842792"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2961141"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2823866"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2861992"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3040879"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3292142"},{"key":"ref23","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref25","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref26","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. NIPS","author":"Khosla"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00304"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01553"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00654"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"ref32","first-page":"321","article-title":"Learning with local and global consistency","volume-title":"Proc. 16th Int. Conf. Neur. Inf. Process. Syst. (NIPS)","author":"Zhou"},{"key":"ref33","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Goldberger"},{"key":"ref34","first-page":"8792","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst. (NIPS)","author":"Zhang"},{"key":"ref35","first-page":"7164","article-title":"How does disagreement help generalization against label corruption","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2023.3339629"},{"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":"10.1109\/TGRS.2025.3549049"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3445136"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3476116"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3300903"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3286422"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2023.3235117"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3243853"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2025.3586282"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3130716"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2994057"},{"key":"ref48","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. NIPS","author":"Caron"},{"key":"ref49","first-page":"1","article-title":"Theoretical analysis of self-training with deep networks on unlabeled data","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wei"},{"key":"ref50","first-page":"1","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.112012"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2867444"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3392264"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3390\/rs14132997"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2755542"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10807682\/11213510.pdf?arnumber=11213510","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:16:12Z","timestamp":1763532972000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11213510\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2025.3624045","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"type":"print","value":"0196-2892"},{"type":"electronic","value":"1558-0644"}],"subject":[],"published":{"date-parts":[[2025]]}}}