{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:32:42Z","timestamp":1787499162295,"version":"build-2736575974"},"reference-count":43,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Science Foundation of China","award":["61605243"],"award-info":[{"award-number":["61605243"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Space debris detection is vital to space missions and space situation awareness. Convolutional neural networks are introduced to detect space debris due to their excellent performance. However, noisy labels, caused by false alarms, exist in space debris detection, and cause ambiguous targets for the training of networks, leading to networks overfitting the noisy labels and losing the ability to detect space debris. To remedy this challenge, we introduce label-noise learning to space debris detection and propose a novel label-noise learning paradigm, termed Co-correcting, to overcome the effects of noisy labels. Co-correcting comprises two identical networks, and the predictions of these networks serve as auxiliary supervised information to mutually correct the noisy labels of their peer networks. In this manner, the effect of noisy labels can be mitigated by the mutual rectification of the two networks. Empirical experiments show that Co-correcting outperforms other state-of-the-art methods of label-noise learning, such as Co-teaching and JoCoR, in space debris detection. Even with a high label noise rate, the network trained via Co-correcting can detect space debris with high detection probability.<\/jats:p>","DOI":"10.3390\/rs14205261","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"5261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Co-Correcting: Combat Noisy Labels in Space Debris Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4163-9842","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaodong","family":"Niu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Sun","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yabo","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s00159-006-0003-9","article-title":"Optical surveys for space debris","volume":"14","author":"Schildknecht","year":"2007","journal-title":"Astron. Astrophys. Rev."},{"key":"ref_2","unstructured":"Thiele, S., and Boley, A. (2021). Investigating the risks of debris-generating ASAT tests in the presence of megaconstellations. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e04692","DOI":"10.1016\/j.heliyon.2020.e04692","article-title":"Debris cloud of India anti-satellite test to Microsat-R satellite","volume":"6","author":"Jiang","year":"2020","journal-title":"Heliyon"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150864","DOI":"10.1109\/ACCESS.2020.3016761","article-title":"Space debris detection using feature learning of candidate regions in optical image sequences","volume":"8","author":"Xi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Schirru, L., Pisanu, T., and Podda, A. (2021). The Ad Hoc Back-End of the BIRALET Radar to Measure Slant-Range and Doppler Shift of Resident Space Objects. Electronics, 10.","DOI":"10.3390\/electronics10050577"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ionescu, L., Rusu-Casandra, A., Bira, C., Tatomirescu, A., Tramandan, I., Scagnoli, R., Istriteanu, D., and Popa, A.E. (2022). Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities. Sensors, 22.","DOI":"10.3390\/s22093546"},{"key":"ref_7","unstructured":"Ender, J., Leushacke, L., Brenner, A., and Wilden, H. (2011, January 7\u20139). Radar techniques for space situational awareness. Proceedings of the 2011 12th International Radar Symposium (IRS), Leipzig, Germany."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TPAMI.1981.4767153","article-title":"Computer tracking of moving point targets in space","volume":"5","author":"Mohanty","year":"1981","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TAES.1983.309401","article-title":"Application of three-dimensional filtering to moving target detection","volume":"6","author":"Reed","year":"1983","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/7.106120","article-title":"A recursive moving-target-indication algorithm for optical image sequences","volume":"26","author":"Reed","year":"1990","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TAES.1985.310548","article-title":"Dynamic programming solution for detecting dim moving targets","volume":"1","author":"Barniv","year":"1985","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TAES.2005.1541440","article-title":"Track-before-detect procedures for early detection of moving target from airborne radars","volume":"41","author":"Buzzi","year":"2005","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.asr.2007.04.033","article-title":"Distinguishing features of CCD astrometry of faint GEO objects","volume":"41","author":"Kouprianov","year":"2008","journal-title":"Adv. Space Res."},{"key":"ref_14","unstructured":"Tagawa, M., Hanada, T., Oda, H., Kurosaki, H., and Yanagisawa, T. (2014, January 2\u201310). Detection algorithm of small and Fast orbital objects using Faint Streaks; application to geosynchronous orbit objects. Proceedings of the 40th COSPAR Scientific Assembly, Moscow, Russia."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1016\/j.asr.2015.09.024","article-title":"Streak detection and analysis pipeline for space-debris optical images","volume":"57","author":"Virtanen","year":"2016","journal-title":"Adv. Space Res."},{"key":"ref_16","unstructured":"Yanagisawa, T., Kurosaki, H., Banno, H., Kitazawa, Y., Uetsuhara, M., and Hanada, T. (2012, January 11\u201314). Comparison between four detection algorithms for GEO objects. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.asr.2015.04.006","article-title":"Improving space debris detection in GEO ring using image deconvolution","volume":"56","author":"Nunez","year":"2015","journal-title":"Adv. Space Res."},{"key":"ref_18","unstructured":"Stoveken, E., and Schildknecht, T. (2005, January 18\u201320). Algorithms for the optical detection of space debris objects. Proceedings of the 4th European Conference on Space Debris, Darmstadt, Germany."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wei, M., Chen, H., Yan, T., Wu, Q., and Xu, B. (2010, January 2\u20134). The detecting methods of geostationary orbit objects. Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, China.","DOI":"10.1109\/IITSI.2010.31"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.asr.2016.08.027","article-title":"An optical survey for space debris on highly eccentric and inclined MEO orbits","volume":"59","author":"Silha","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.asr.2003.01.009","article-title":"Optical observations of space debris in GEO and in highly-eccentric orbits","volume":"34","author":"Schildknecht","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0273-1177(98)00229-4","article-title":"Optical surveys of space debris in GEO","volume":"23","author":"Schildknecht","year":"1999","journal-title":"Adv. Space Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/0273-1177(95)98752-A","article-title":"Algorithms for ground based optical detection of space debris","volume":"16","author":"Schildknecht","year":"1995","journal-title":"Adv. Space Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1088\/1674-4527\/13\/5\/011","article-title":"A new source extraction algorithm for optical space debris observation","volume":"13","author":"Sun","year":"2013","journal-title":"Res. Astron. Astrophys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2815890","DOI":"10.1155\/2019\/2815890","article-title":"Effect analysis of optical masking algorithm for geo space debris detection","volume":"2019","author":"Kong","year":"2019","journal-title":"Int. J. Opt."},{"key":"ref_26","first-page":"111","article-title":"A new method of small target detection based on neural network","volume":"Volume 10608","author":"Hu","year":"2018","journal-title":"Proceedings of the MIPPR 2017: Automatic Target Recognition and Navigation"},{"key":"ref_27","unstructured":"Varela, L., Boucheron, L., Malone, N., and Spurlock, N. (2019, January 17\u201320). Streak detection in wide field of view images using Convolutional Neural Networks (CNNs). Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, HI, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3847\/1538-3881\/ab800a","article-title":"Detection and classification of astronomical targets with deep neural networks in wide-field small aperture telescopes","volume":"159","author":"Jia","year":"2020","journal-title":"Astron. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wei, H., Feng, L., Chen, X., and An, B. (2020, January 13\u201319). Combating noisy labels by agreement: A joint training method with co-regularization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref_31","unstructured":"Arpit, D., Jastrzebski, S.K., Ballas, N., Krueger, D., Bengio, E., Kanwal, M.S., Maharaj, T., Fischer, A., Courville, A.C., and Bengio, Y. (2017, January 6\u201311). A Closer Look at Memorization in Deep Networks. Proceedings of the ICML, Sydney, Australia."},{"key":"ref_32","unstructured":"Han, B., Yao, J., Gang, N., Zhou, M., Tsang, I., Zhang, Y., and Sugiyama, M. (2018, January 3\u20138). Masking: A new perspective of noisy supervision. Proceedings of the NeurIPS, Montreal, QC, Canada."},{"key":"ref_33","unstructured":"Li, M., Soltanolkotabi, M., and Oymak, S. (2020, January 26\u201328). Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks. Proceedings of the International Conference on Artificial Intelligence and Statistics PMLR, Online."},{"key":"ref_34","unstructured":"Yu, X., Han, B., Yao, J., Niu, G., Tsang, I.W., and Sugiyama, M. How does Disagreement Help Generalization against Label Corruption? In Proceedings of the ICML, Long Beach, CA, USA, 9\u201315 June 2019."},{"key":"ref_35","unstructured":"Goldberger, J., and Ben-Reuven, E. (2017, January 24\u201326). Training deep neural-networks using a noise adaptation layer. Proceedings of the ICLR (Poster). OpenReview.net, Toulon, France."},{"key":"ref_36","first-page":"6838","article-title":"Are anchor points really indispensable in label-noise learning?","volume":"32","author":"Xia","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","first-page":"1196","article-title":"Learning with noisy labels","volume":"26","author":"Natarajan","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1007\/s10994-018-5715-3","article-title":"Learning from binary labels with instance-dependent noise","volume":"107","author":"Menon","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_39","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., and Fei-Fei, L. (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_40","unstructured":"Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I.W., and Sugiyama, M. (2018, January 3\u20138). Co-teaching: Robust training of deep neural networks with extremely noisy labels. Proceedings of the NeurIPS, Montreal, QC, Canada."},{"key":"ref_41","unstructured":"Malach, E., and Shalev-Shwartz, S. (2017, January 4\u20139). Decoupling \u201cwhen to update\u201d from \u201chow to update\u201d. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1109\/TIP.2002.801126","article-title":"On the origin of the bilateral filter and ways to improve it","volume":"11","author":"Elad","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_25"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:44Z","timestamp":1760144324000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":43,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205261"],"URL":"https:\/\/doi.org\/10.3390\/rs14205261","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,21]]}}}