{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:55:59Z","timestamp":1785113759983,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Service Hydrographique et Oc\u00e9anographique de la Marine (SHOM)","award":["111222"],"award-info":[{"award-number":["111222"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%\/76% accuracy in 2018\/2020) and first-year ice (86%\/84% accuracy in 2018\/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available.<\/jats:p>","DOI":"10.3390\/rs12132165","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T03:13:51Z","timestamp":1594091631000},"page":"2165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9935-3145","authenticated-orcid":false,"given":"Hugo","family":"Boulze","sequence":"first","affiliation":[{"name":"\u00c9cole Nationale des Sciences G\u00e9ographiques, Univ. Gustave Eiffel, 77455 Marne-la-Vall\u00e9e, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3601-1161","authenticated-orcid":false,"given":"Anton","family":"Korosov","sequence":"additional","affiliation":[{"name":"Nansen Environmental and Remote Sensing Center, 5006 Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0634-1482","authenticated-orcid":false,"given":"Julien","family":"Brajard","sequence":"additional","affiliation":[{"name":"Nansen Environmental and Remote Sensing Center, 5006 Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"key":"ref_1","unstructured":"Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology (2014). Ice Chart Colour Code Standard, World Meteorological Organization & Intergovernmental Oceanographic Commission. Version 1.0."},{"key":"ref_2","unstructured":"(2020, June 04). Sentinel-1 SAR, ESA. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-1-sar."},{"key":"ref_3","unstructured":"Jackson, C.R., and Apel, J.R. (2004). Synthetic Aperture Radar Marine User\u2019s Manual."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5529","DOI":"10.1109\/TGRS.2013.2290231","article-title":"Automated Ice\u2013Water Classification Using Dual Polarization SAR Satellite Imagery","volume":"52","author":"Leigh","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"401","DOI":"10.5194\/tc-10-401-2016","article-title":"Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band","volume":"10","author":"Fors","year":"2016","journal-title":"Cryosphere"},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"3694","DOI":"10.1109\/TGRS.2018.2886685","article-title":"Improved Retrieval of Ice and Open Water From Sequential RADARSAT-2 Images","volume":"57","author":"Komarov","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/JSTARS.2020.2977506","article-title":"First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea","volume":"13","author":"Aldenhoff","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.5194\/tc-8-1639-2014","article-title":"A sea ice concentration estimation algorithm utilizing radiometer and SAR data","volume":"8","author":"Karvonen","year":"2014","journal-title":"Cryosphere"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1109\/TGRS.2017.2655567","article-title":"Baltic Sea Ice Concentration Estimation Using SENTINEL-1 SAR and AMSR2 Microwave Radiometer Data","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9109","DOI":"10.1109\/TGRS.2019.2924868","article-title":"Detection of First-Year and Multi-Year Sea Ice from Dual-Polarization SAR Images Under Cold Conditions","volume":"57","author":"Komarov","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lohse, J., Doulgeris, A.P., and Dierking, W. (2019). An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11131574"},{"key":"ref_15","first-page":"1","article-title":"Classification of Sea Ice Types in Sentinel-1 SAR images","volume":"2019","author":"Park","year":"2019","journal-title":"Cryosphere Discuss."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1109\/TGRS.2017.2765248","article-title":"Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel","volume":"56","author":"Park","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1109\/TGRS.2018.2889381","article-title":"Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel Images","volume":"57","author":"Park","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"},{"key":"ref_19","unstructured":"Malmgren-Hansen, D., Nielsen, A.A., Kreiner, M.B., Saldo, R., Skriver, H., Toudal Pedersen, L., Lavelle, J., and Buus-Hinkler, J. (2019, January 13\u201317). High-Resolution Sea Ice Maps with Convolutional Neural Networks. Proceedings of the 2019 ESA Living Planet Symposium, LPS 2019, Milan, Italy."},{"key":"ref_20","unstructured":"Seger, C. (2018). An Investigation of Categorical Variable Encoding Techniques in Machine Learning: Binary Versus One-Hot and Feature Hashing, KTH, School of Electrical Engineering and Computer Science (EECS). Technical Report 2018:596."},{"key":"ref_21","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA."},{"key":"ref_22","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_23","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_24","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_26","unstructured":"Korosov, A., and Boulze, H. (2020, June 04). s1_icetype_cnn. Available online: https:\/\/doi.org\/10.5281\/zenodo.3828992."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e39","DOI":"10.5334\/jors.120","article-title":"Nansat: A Scientist-Orientated Python Package for Geospatial Data Processing","volume":"4","author":"Korosov","year":"2016","journal-title":"J. Open Res. Softw."},{"key":"ref_28","unstructured":"(2020, June 04). European Centre for Medium-Range Weather Forecasts. Available online: https:\/\/www.ecmwf.int\/en\/forecasts."},{"key":"ref_29","first-page":"281","article-title":"Random Search for Hyper-parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tsai, Y.L.S., Dietz, A., Oppelt, N., and Kuenzer, C. (2019). Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sens., 11.","DOI":"10.3390\/rs11080895"},{"key":"ref_31","first-page":"60","article-title":"The International Code for Ships Operating in Polar Waters: Finalization, Adoption and Law of the Sea Implications","volume":"7","author":"Jensen","year":"2016","journal-title":"Arct. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1111\/j.1600-0870.2009.00417.x","article-title":"Asynchronous data assimilation with the EnKF","volume":"62","author":"Sakov","year":"2010","journal-title":"Tellus A"},{"key":"ref_33","unstructured":"Sensoy, M., Kaplan, L., and Kandemir, M. (2018, January 3\u20138). Evidential deep learning to quantify classification uncertainty. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_34","unstructured":"Gal, Y., and Ghahramani, Z. (2016, January 19\u201324). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Proceedings of the International Conference on Machine Learning, New York, NY, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2165\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:48:19Z","timestamp":1760176099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":34,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12132165"],"URL":"https:\/\/doi.org\/10.3390\/rs12132165","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,7]]}}}