{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:12:47Z","timestamp":1775279567869,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T00:00:00Z","timestamp":1542672000000},"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":["61873279 and 61563018"],"award-info":[{"award-number":["61873279 and 61563018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["16CX02048A"],"award-info":[{"award-number":["16CX02048A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of Shandong Province","award":["2018GSF120020"],"award-info":[{"award-number":["2018GSF120020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The proliferation of remote sensing imagery motivates a surge of research interest in image processing such as feature extraction and scene recognition, etc. Among them, scene recognition (classification) is a typical learning task that focuses on exploiting annotated images to infer the category of an unlabeled image. Existing scene classification algorithms predominantly focus on static data and are designed to learn discriminant information from clean data. They, however, suffer from two major shortcomings, i.e., the noisy label may negatively affect the learning procedure and learning from scratch may lead to a huge computational burden. Thus, they are not able to handle large-scale remote sensing images, in terms of both recognition accuracy and computational cost. To address this problem, in the paper, we propose a noise-resilient online classification algorithm, which is scalable and robust to noisy labels. Specifically, ramp loss is employed as loss function to alleviate the negative affect of noisy labels, and we iteratively optimize the decision function in Reproducing Kernel Hilbert Space under the framework of Online Gradient Descent (OGD). Experiments on both synthetic and real-world data sets demonstrate that the proposed noise-resilient online classification algorithm is more robust and sparser than state-of-the-art online classification algorithms.<\/jats:p>","DOI":"10.3390\/rs10111836","type":"journal-article","created":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T11:23:27Z","timestamp":1542799407000},"page":"1836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Noise-Resilient Online Learning Algorithm for Scene Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9385-5977","authenticated-orcid":false,"given":"Ling","family":"Jian","sequence":"first","affiliation":[{"name":"College of Science, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Fuhao","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Science, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Peng","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Yunquan","family":"Song","sequence":"additional","affiliation":[{"name":"College of Science, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Shihua","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Statistics, Jiangxi University of Finance &amp; Economics, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1109\/TGRS.2014.2357078","article-title":"Saliency-guided unsupervised feature learning for scene classification","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Liu, F. (2018). Dense connectivity based two-stream deep feature fusion framework for aerial scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10071158"},{"key":"ref_3","first-page":"2","article-title":"Integration of remote sensing and GIS techniques for flood monitoring and damage assessment: A case study of naogaon district","volume":"7","author":"Faisal","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_4","unstructured":"Bi, S., Lin, X., Wu, Z., and Yang, S. (2018). Development technology of principle prototype of high-resolution quantum remote sensing imaging. Quantum Sensing and Nano Electronics and Photonics XV, International Society for Optics and Photonics."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weng, Q., Quattrochi, D., and Gamba, P.E. (2018). Urban Remote Sensing, CRC Press.","DOI":"10.1201\/9781315166612"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mukherjee, A.B., Krishna, A.P., and Patel, N. (2018). Application of remote sensing technology, GIS and AHP-TOPSIS model to quantify urban landscape vulnerability to land use transformation. Information and Communication Technology for Sustainable Development, Springer.","DOI":"10.1007\/978-981-10-3920-1_4"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, P., Ren, P., and Zhang, X. (2018). Region-wise deep feature representation for remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10060871"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1109\/TGRS.2013.2268736","article-title":"Remote sensing image retrieval with global morphological texture descriptors","volume":"52","author":"Aptoula","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2012.2205158","article-title":"Geographic image retrieval using local invariant features","volume":"52","author":"Yang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, Y., and Tao, C. (2016). Content-based high-resolution remote sensing image retrieval via unsupervised feature learning and collaborative affinity metric fusion. Remote Sens., 8.","DOI":"10.3390\/rs8090709"},{"key":"ref_13","first-page":"23","article-title":"An unsupervised convolutional feature fusion network for deep representation of remote sensing images","volume":"15","author":"Yu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Q., Liu, S., Chanussot, J., and Li, X. (2018). Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2018.2864987"},{"key":"ref_15","unstructured":"Ma, X., Liu, W., Li, S., Tao, D., and Zhou, Y. (2018). Hypergraph-Laplacian regularization for remotely sensed image recognition. IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Q., He, X., and Li, X. (2018). Locality and structure regularized low rank representation for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2018.2862899"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","article-title":"Modeling the shape of the scene: A holistic representation of the spatial envelope","volume":"42","author":"Oliva","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised deep feature extraction for remote sensing image classification","volume":"54","author":"Romero","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Li, F.F., Fergus, R., and Perona, P. (July, January 27). Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA."},{"key":"ref_21","first-page":"2076","article-title":"Budget online learning algorithm for least squares SVM","volume":"28","author":"Jian","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.asoc.2016.12.004","article-title":"A chunk updating LS-SVMs based on block Gaussian elimination method","volume":"51","author":"Song","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_23","unstructured":"Hu, J., Sun, Z., and Li, B. (2017, January 4\u20136). Online user modeling for interactive streaming image classification. Proceedings of the Conference on Multimedia Modeling, Reykjavik, Iceland."},{"key":"ref_24","unstructured":"Meng, J.E., Venkatesan, R., and Ning, W. (2017, January 9\u201312). An online universal classifier for binary, multi-class and multi-label classification. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, P., and Hoi, S.C.H. (2013, January 11\u201314). Cost-sensitive online active learning with application to malicious URL detection. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487647"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.cam.2018.08.020","article-title":"Laplace error penalty-based M-type model detection for a class of high dimensional semiparametric models","volume":"347","author":"Jian","year":"2019","journal-title":"J. Comput. Appl. Math."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1023\/A:1007697429651","article-title":"Improved generalization through explicit optimization of margins","volume":"38","author":"Mason","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1198\/016214503000000639","article-title":"On \u03c8-learning","volume":"98","author":"Shen","year":"2003","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Collobert, R., Sinz, F., Weston, J., and Bottou, L. (2006, January 25\u201329). Trading convexity for scalability. Proceedings of the ACM International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143870"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1198\/016214507000000617","article-title":"Robust truncated hinge loss support vector machines","volume":"102","author":"Wu","year":"2007","journal-title":"Publ. Am. Stat. Assoc."},{"key":"ref_31","first-page":"1071","article-title":"Sparseness of support vector machines","volume":"4","author":"Steinwart","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2015). Data Mining: The Textbook, Springer.","DOI":"10.1007\/978-3-319-14142-8"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1137\/060666998","article-title":"The Forgetron: A kernel-based Perceptron on a budget","volume":"37","author":"Dekel","year":"2008","journal-title":"SIAM J. Comput."},{"key":"ref_34","first-page":"551","article-title":"Online passive-aggressive algorithms","volume":"7","author":"Crammer","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","unstructured":"Francesco, O., Joseph, K., and Barbara, C. (2008, January 5\u20139). The projectron: A bounded kernel-based Perceptron. Proceedings of the International Conference on Machine Learning, Helsinki, Finland."},{"key":"ref_36","unstructured":"Zhao, P., Wang, J., and Wu, P. (July, January 26). Fast bounded online gradient descent algorithms for scalable kernel-based online learning. Proceedings of the International Conference on Machine Learning, Edinburgh, UK."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s10618-017-0533-y","article-title":"Toward online node classification on streaming networks","volume":"32","author":"Jian","year":"2018","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, H., Douze, M., Schmid, C., and P\u00e9rez, P. (2010, January 13\u201318). Aggregating local descriptors into a compact image representation. Proceedings of the Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1109\/TIP.2009.2024578","article-title":"n-SIFT: n-dimensional scale invariant feature transform","volume":"18","author":"Cheung","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A.J., and Bach, F. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1162\/08997660360581958","article-title":"The concave-convex procedure","volume":"15","author":"Yuille","year":"2003","journal-title":"Neural Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1561\/2200000018","article-title":"Online learning and online convex optimization","volume":"4","year":"2012","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_43","first-page":"265","article-title":"On the algorithmic implementation of multiclass kernel-based vector machines","volume":"2","author":"Crammer","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10107-010-0420-4","article-title":"Pegasos: Primal estimated sub-gradient solver for SVM","volume":"127","author":"Singer","year":"2011","journal-title":"Math. Program."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","article-title":"A fast and accurate online sequential learning algorithm for feedforward networks","volume":"17","author":"Liang","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_47","first-page":"951","article-title":"Ultraconservative online algorithms for multiclass problems","volume":"3","author":"Crammer","year":"2003","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1836\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T04:36:52Z","timestamp":1775277412000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,20]]},"references-count":47,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111836"],"URL":"https:\/\/doi.org\/10.3390\/rs10111836","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,20]]}}}