{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:09:28Z","timestamp":1773518968685,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2019GGX101033"],"award-info":[{"award-number":["2019GGX101033"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701513"],"award-info":[{"award-number":["41701513"]}],"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":["41772350"],"award-info":[{"award-number":["41772350"]}],"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":["62071491"],"award-info":[{"award-number":["62071491"]}],"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":["61371189"],"award-info":[{"award-number":["61371189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea ice information in the Arctic region is essential for climatic change monitoring and ship navigation. Although many sea ice classification methods have been put forward, the accuracy and usability of classification systems can still be improved. In this paper, a two-round weight voting strategy-based ensemble learning method is proposed for refining sea ice classification. The proposed method includes three main steps. (1) The preferable features of sea ice are constituted by polarization features (HH, HV, HH\/HV) and the top six GLCM-derived texture features via a random forest. (2) The initial classification maps can then be generated by an ensemble learning method, which includes six base classifiers (NB, DT, KNN, LR, ANN, and SVM). The tuned voting weights by a genetic algorithm are employed to obtain the category score matrix and, further, the first coarse classification result. (3) Some pixels may be misclassified due to their corresponding numerically close score value. By introducing an experiential score threshold, each pixel is identified as a fuzzy or an explicit pixel. The fuzzy pixels can then be further rectified based on the local similarity of the neighboring explicit pixels, thereby yielding the final precise classification result. The proposed method was examined on 18 Sentinel-1 EW images, which were captured in the Northeast Passage from November 2019 to April 2020. The experiments show that the proposed method can effectively maintain the edge profile of sea ice and restrain noise from SAR. It is superior to the current mainstream ensemble learning algorithms with the overall accuracy reaching 97%. The main contribution of this study is proposing a superior weight voting strategy in the ensemble learning method for sea ice classification of Sentinel-1 imagery, which is of great significance for guiding secure ship navigation and ice hazard forecasting in winter.<\/jats:p>","DOI":"10.3390\/rs13193945","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3945","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2565-1013","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Linghui","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2420-8012","authenticated-orcid":false,"given":"Dongmei","family":"Song","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Zhongwei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"The North China Sea Marine Forecasting Center, State Oceanic Administration, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zakhvatkina, N., Smirnov, V., and Bychkova, I. (2019). Satellite SAR data-based sea ice classification: An overview. Geosciences, 9.","DOI":"10.3390\/geosciences9040152"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103001","DOI":"10.1088\/1748-9326\/aade56","article-title":"Changing state of Arctic sea ice across all seasons","volume":"13","author":"Stroeve","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1038\/s41558-019-0551-4","article-title":"Minimal influence of reduced Arctic sea ice on coincident cold winters in mid-latitudes","volume":"9","author":"Blackport","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/s41561-018-0059-y","article-title":"Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models","volume":"11","author":"Screen","year":"2018","journal-title":"Nat. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1038\/s41558-018-0379-3","article-title":"A reconciled estimate of the influence of Arctic sea-ice loss on recent Eurasian cooling","volume":"9","author":"Mori","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1038\/nature09051","article-title":"The central role of diminishing sea ice in recent Arctic temperature amplification","volume":"464","author":"Screen","year":"2010","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/nature10705","article-title":"Changing Arctic ocean freshwater pathways","volume":"481","author":"Morison","year":"2012","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1126\/science.1235225","article-title":"Ecological consequences of sea-ice decline","volume":"341","author":"Post","year":"2013","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, L., and Li, J. (2015). Mapping Sea Ice from Satellite SAR Imagery. Monitoring and Modeling of Global Changes: A Geomatics Perspective, Springer.","DOI":"10.1007\/978-94-017-9813-6_6"},{"key":"ref_10","unstructured":"Office of the Auditor General of Canada (2014). Marine navigation in the Canadian Arctic. Fall Report of the Commissioner Environment and Sustainable Development, Office of the Auditor General of Canada."},{"key":"ref_11","first-page":"245","article-title":"Ice conditions for maritime traffic in the Baltic Sea in future climate","volume":"22","author":"Pemberton","year":"2017","journal-title":"Boreal Environ. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.marpol.2009.10.009","article-title":"The implications of Arctic sea ice decline on shipping","volume":"34","author":"Ho","year":"2010","journal-title":"Mar. Policy"},{"key":"ref_13","unstructured":"IMO (2014). International Code for Ships Operating in Polar Waters (Polar Code), IMO Resolution MSC."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1109\/TGRS.2018.2809504","article-title":"Arctic sea ice characterization using spaceborne fully polarimetric L-, C-, and X-band SAR with validation by airborne measurements","volume":"56","author":"Singha","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3189\/S0022143000003774","article-title":"Winter sea-ice mapping from multi-parameter synthetic-aperture radar data","volume":"40","author":"Rignot","year":"1994","journal-title":"J. Glaciol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/TGRS.2010.2060729","article-title":"Sea Ice emissivity modeling at L-band and application to 2007 pol-ice campaign field data","volume":"49","author":"Mills","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Scharien, R.K., Geldsetzer, T., Nasonova, S., Cafarella, S., and Tavri, A. (2018, January 22\u201327). Assessment of seasonal sea ice type and roughness regime discrimination using a unique C-and L-band SAR database. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519439"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2017.10.032","article-title":"X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring","volume":"204","author":"Johansson","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dabboor, M., Montpetit, B., Howell, S., and Haas, C. (2017). Improving sea ice characterization in dry ice winter conditions using polarimetric parameters from C-and L-band SAR data. Remote Sens., 9.","DOI":"10.3390\/rs9121270"},{"key":"ref_20","unstructured":"Haverkamp, D., Soh, L.K., and Tsatsoulis, C. (1993, January 18\u201321). A dynamic local thresholding technique for sea ice classification. Proceedings of the IGARSS\u201993-IEEE International Geoscience and Remote Sensing Symposium, Tokyo, Japan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1029\/91JC02652","article-title":"Identification of sea ice types in spaceborne synthetic aperture radar data","volume":"97","author":"Kwok","year":"1992","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_22","unstructured":"Simil\u00e4, M., Dinessen, F., Hughes, N.E., and M\u00e4kynen, M. (2013, January 9\u201313). Ice edge detection with dual-polarized SAR data. Proceedings of the 22nd International Conference on Port and Ocean Engineering under Arctic Conditions, Espoo, Finland."},{"key":"ref_23","first-page":"36","article-title":"Use of RADARSAT data in the Canadian ice service","volume":"24","author":"Ramsay","year":"1998","journal-title":"Remote Sens."},{"key":"ref_24","first-page":"105","article-title":"Microwave remote sensing, sea ice and Arctic climate","volume":"61","author":"Barber","year":"2005","journal-title":"Phys. Can."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/36.368223","article-title":"A comprehensive, automated approach to determining sea ice thickness from SAR data","volume":"33","author":"Haverkamp","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1109\/JSTARS.2018.2806640","article-title":"Semi-automated segmentation of Sentinel-1 SAR imagery for mapping sea ice in Labrador coast","volume":"11","author":"Tan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1017\/aog.2018.7","article-title":"Comparison of ice\/water classification in Fram Strait from C- and L-band SAR imagery","volume":"59","author":"Aldenhoff","year":"2018","journal-title":"Ann. Glaciol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2014.2365215","article-title":"SVM-based sea ice classification using textural features and concentration from RADARSAT-2 dual-pol ScanSAR data","volume":"8","author":"Liu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.5194\/tc-11-33-2017","article-title":"Operational algorithm for ice\u2013water classification on dual-polarized RADARSAT-2 images","volume":"11","author":"Zakhvatkina","year":"2017","journal-title":"Cryosphere"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1109\/TGRS.2005.846882","article-title":"Multisensor approach to automated classification of sea ice image data","volume":"43","author":"Bogdanov","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Boulze, H., Korosov, A., and Brajard, J. (2020). Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks. Remote Sens., 12.","DOI":"10.3390\/rs12132165"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kruk, R., Fuller, M.C., Komarov, A.S., Isleifson, D., and Jeffrey, I. (2020). Proof of concept for sea ice stage of development classification using deep learning. Remote Sens., 12.","DOI":"10.3390\/rs12152486"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.5194\/essd-13-2723-2021","article-title":"Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning","volume":"13","author":"Wang","year":"2021","journal-title":"Earth Syst. Sci. Data Dis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vasilakos, C., Kavroudakis, D., and Georganta, A. (2020). Machine learning classification ensemble of multitemporal Sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sens., 12.","DOI":"10.3390\/rs12122005"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.1080\/01431161.2018.1446566","article-title":"A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification","volume":"39","author":"Shen","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.5815\/ijigsp.2016.12.03","article-title":"Remote sensing textual image classification based on ensemble learning","volume":"8","author":"Yang","year":"2016","journal-title":"Int. J. Image Graph. Signal Process."},{"key":"ref_37","unstructured":"Veci, L., Lu, J., Prats-Iraola, P., Scheiber, R., Collard, F., Fomferra, N., and Engdahl, M. (2014, January 13\u201318). The Sentinel-1 toolbox. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tan, W., Liao, R., Du, Y., Lu, J., and Li, J. (2015, January 26\u201331). Improving urban impervious surface classification by combining Landsat and PolSAR images: A case study in Kitchener-Waterloo, Ontario, Canada. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326169"},{"key":"ref_42","unstructured":"Rish, I. (2001, January 6). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hassoun, M.H. (1995). Fundamentals of Artificial Neural Networks, MIT press.","DOI":"10.1109\/JPROC.1996.503146"},{"key":"ref_45","unstructured":"Soucy, P., and Mineau, G.W. (December, January 29). A simple KNN algorithm for text categorization. Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","first-page":"841","article-title":"On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes","volume":"14","author":"Jordan","year":"2002","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1016\/S0031-3203(02)00121-8","article-title":"Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets","volume":"36","author":"Bryll","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1021\/ci0500379","article-title":"Boosting: An ensemble learning tool for compound classification and QSAR modeling","volume":"45","author":"Svetnik","year":"2005","journal-title":"J. Chem. Inf. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.asoc.2019.01.015","article-title":"Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection","volume":"77","author":"Wang","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.aca.2004.12.024","article-title":"Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization","volume":"544","author":"Melssen","year":"2005","journal-title":"Anal. Chim. Acta"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.4236\/am.2012.330207","article-title":"Parameters optimization using genetic algorithms in support vector regression for sales volume forecasting","volume":"03","author":"Yuan","year":"2012","journal-title":"Appl. Math."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Han, C., Zhang, L., and Wang, X. (2016, January 10\u201315). Polarimetric SAR image classification based on selective ensemble learning of sparse representation. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730295"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.tourman.2005.12.018","article-title":"Support vector regression with genetic algorithms in forecasting tourism demand","volume":"28","author":"Chen","year":"2007","journal-title":"Tour. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3945\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:08:45Z","timestamp":1760166525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3945"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,2]]},"references-count":54,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193945"],"URL":"https:\/\/doi.org\/10.3390\/rs13193945","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,2]]}}}