{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:53:36Z","timestamp":1776182016224,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571230"],"award-info":[{"award-number":["61571230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871226"],"award-info":[{"award-number":["61871226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906093"],"award-info":[{"award-number":["61906093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Social Developing Project of China","award":["BE2018727"],"award-info":[{"award-number":["BE2018727"]}]},{"name":"Jiangsu Provincial Nature Science Foundations of China","award":["BK20190451"],"award-info":[{"award-number":["BK20190451"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30918011104"],"award-info":[{"award-number":["30918011104"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30920021134"],"award-info":[{"award-number":["30920021134"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Major Research Plan of China","award":["2016YFF0103604"],"award-info":[{"award-number":["2016YFF0103604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep-learning methods rely on massive labeled data, which has become one of the main impediments in hyperspectral image change detection (HSI-CD). To resolve this problem, pseudo-labels generated by traditional methods are widely used to drive model learning. In this paper, we propose a mutual teaching approach with momentum correction for unsupervised HSI-CD to cope with noise in pseudo-labels, which is harmful for model training. First, we adopt two structurally identical models simultaneously, allowing them to select high-confidence samples for each other to suppress self-confidence bias, and continuously update pseudo-labels during iterations to fine-tune the models. Furthermore, a new group confidence-based sample filtering method is designed to obtain reliable training samples for HSI. This method considers both the quality and diversity of the selected samples by determining the confidence of each group instead of single instances. Finally, to better extract the spatial\u2013temporal spectral features of bitemporal HSIs, a 3D convolutional neural network (3DCNN) is designed as an HSI-CD classifier and the basic network of our framework. Due to mutual teaching and dynamic label learning, pseudo-labels can be continuously updated and refined in iterations, and thus, the proposed method can achieve a better performance compared with those with fixed pseudo-labels. Experimental results on several HSI datasets demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/rs14041000","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:23:29Z","timestamp":1645431809000},"page":"1000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Mutual Teaching Framework with Momentum Correction for Unsupervised Hyperspectral Image Change Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Jia","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Ling","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Zhihui","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-9384","authenticated-orcid":false,"given":"Liang","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2017.03.009","article-title":"Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring","volume":"128","author":"Scafutto","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","first-page":"287","article-title":"Hyperspectral remote sensing applied to mineral exploration in southern Peru: A multiple data integration approach in the Chapi Chiara gold prospect","volume":"64","author":"Carrino","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4117","DOI":"10.1109\/JSTARS.2016.2577339","article-title":"Crop classification based on feature band set construction and object-oriented approach using hyperspectral images","volume":"9","author":"Zhang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review article digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TGRS.2006.885408","article-title":"A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain","volume":"45","author":"Bovolo","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(97)00162-4","article-title":"Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies","volume":"64","author":"Nielsen","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TIP.2006.888195","article-title":"The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data","volume":"16","author":"Nielsen","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Review ArticleDigital change detection methods in ecosystem monitoring: A review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.1109\/TGRS.2013.2266673","article-title":"Slow feature analysis for change detection in multispectral imagery","volume":"52","author":"Wu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1016\/j.rse.2007.08.012","article-title":"Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: A study of 1992 and 2001 US National Land Cover Database changes","volume":"112","author":"Ahlqvist","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/LGRS.2019.2892432","article-title":"A post-classification comparison method for SAR and optical images change detection","volume":"16","author":"Wan","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112308","DOI":"10.1016\/j.rse.2021.112308","article-title":"Change detection using deep learning approach with object-based image analysis","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery","volume":"57","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9976","DOI":"10.1109\/TGRS.2019.2930682","article-title":"Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images","volume":"57","author":"Du","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, X., Yuan, Z., and Wang, Q. (2019). Unsupervised deep noise modeling for hyperspectral image change detection. Remote Sens., 11.","DOI":"10.3390\/rs11030258"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9012","DOI":"10.1109\/JSTARS.2021.3108777","article-title":"Unsupervised Hyperspectral Image Change Detection via Deep Learning Self-generated Credible Labels","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/3446776","article-title":"Understanding deep learning (still) requires rethinking generalization","volume":"64","author":"Zhang","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_23","unstructured":"Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., and Sugiyama, M. (2019, January 10\u201315). How does disagreement help generalization against label corruption?. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_24","unstructured":"Yao, Q., Yang, H., Han, B., Niu, G., and Kwok, J.T.Y. (2020, January 13\u201318). Searching to exploit memorization effect in learning with noisy labels. Proceedings of the International Conference on Machine Learning, Virtual Event."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.sigpro.2017.07.023","article-title":"A self-paced learning algorithm for change detection in synthetic aperture radar images","volume":"142","author":"Shang","year":"2018","journal-title":"Signal Proc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TGRS.2019.2951441","article-title":"Group self-paced learning with a time-varying regularizer for unsupervised change detection","volume":"58","author":"Gong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M. (2018, January 10\u201315). Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. Proceedings of the International Conference on Neural Information Processing Systems, Stockholm, Sweden."},{"key":"ref_28","unstructured":"Li, P., Xu, Y., Wei, Y., and Yang, Y. (2020). Self-correction for human parsing. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zheng, G., Awadallah, A.H., and Dumais, S. (2021, January 2\u20139). Meta label correction for noisy label learning. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event.","DOI":"10.1609\/aaai.v35i12.17319"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TNNLS.2016.2636227","article-title":"A deep convolutional coupling network for change detection based on heterogeneous optical and radar images","volume":"29","author":"Liu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1109\/TGRS.2018.2872509","article-title":"Unsupervised difference representation learning for detecting multiple types of changes in multitemporal remote sensing images","volume":"57","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"4701514","article-title":"A Probabilistic Model Based on Bipartite Convolutional Neural Network for Unsupervised Change Detection","volume":"60","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNNLS.2015.2435783","article-title":"Change detection in synthetic aperture radar images based on deep neural networks","volume":"27","author":"Gong","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TGRS.2019.2901945","article-title":"A deep learning method for change detection in synthetic aperture radar images","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Goldberger, J., and Ben-Reuven, E. (2016, January 2\u20134). Training deep neural-networks using a noise adaptation layer. Proceedings of the International Conference of Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kumar, H., and Sastry, P. (2017, January 4\u20139). Robust loss functions under label noise for deep neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, K.H., He, X., Zhang, L., and Yang, L. (2018, January 18\u201322). Cleannet: Transfer learning for scalable image classifier training with label noise. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00571"},{"key":"ref_38","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., and Li, F.-F. (2018, January 10\u201315). Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Blum, A., and Mitchell, T. (1998, January 24\u201326). Combining labeled and unlabeled data with co-training. Proceedings of the Eleventh Annual Conference on Computational Learning Theory, Madison, WI, USA.","DOI":"10.1145\/279943.279962"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiang, T., Hospedales, T.M., and Lu, H. (2018, January 18\u201322). Deep mutual learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00454"},{"key":"ref_41","unstructured":"Ge, Y., Chen, D., and Li, H. (2020, January 26\u201330). Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, F., Li, K., Zhong, Z., Luo, Z., Sun, X., Cheng, H., Guo, X., Huang, F., Ji, R., and Li, S. (2020, January 7\u201312). Asymmetric co-teaching for unsupervised cross-domain person re-identification. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6950"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3580","DOI":"10.1109\/TMI.2021.3091178","article-title":"Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction","volume":"40","author":"Liu","year":"2021","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_44","first-page":"5510417","article-title":"A Mutual Guide Framework for Training Hyperspectral Image Classifiers with Small Data","volume":"60","author":"Tai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2015.02.005","article-title":"Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis","volume":"107","author":"Volpi","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/1000\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:22:24Z","timestamp":1760134944000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/1000"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,18]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14041000"],"URL":"https:\/\/doi.org\/10.3390\/rs14041000","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,18]]}}}