{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:30:17Z","timestamp":1769841017556,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T00:00:00Z","timestamp":1596412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.<\/jats:p>","DOI":"10.3390\/rs12152486","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T07:45:57Z","timestamp":1596440757000},"page":"2486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Ryan","family":"Kruk","sequence":"first","affiliation":[{"name":"Department of ECE, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3599-256X","authenticated-orcid":false,"given":"M. Christopher","family":"Fuller","sequence":"additional","affiliation":[{"name":"Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"},{"name":"Cryosphere and Climate Research Group, University of Calgary, Calgary, AB T2N 1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4645-2104","authenticated-orcid":false,"given":"Alexander S.","family":"Komarov","sequence":"additional","affiliation":[{"name":"Data Assimilation and Satellite Meteorology Research Section, Environment and Climate Change Canada, Ottawa, ON K1A 0H3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dustin","family":"Isleifson","sequence":"additional","affiliation":[{"name":"Department of ECE, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"},{"name":"Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1312-3248","authenticated-orcid":false,"given":"Ian","family":"Jeffrey","sequence":"additional","affiliation":[{"name":"Department of ECE, University of Manitoba, Winnipeg, MB R3T 5V6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1109\/TGRS.2019.2962656","article-title":"Assimilation of SAR Ice and Open Water Retrievals in Environment and Climate Change Canada Regional Ice-Ocean Prediction System","volume":"58","author":"Komarov","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","unstructured":"Carter, N.A., Dawson, J., Joyce, J., and Ogilvie, A. (2020, July 31). Arctic Corridors and Northern Voices: Governing Marine Transportation in the Canadian Arctic (Arviat, Nunavut Community Report). Available online: https:\/\/ruor.uottawa.ca\/handle\/10393\/36924."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1002\/2017GL076587","article-title":"Increasing mobility of high Arctic sea ice increases marine hazards off the east coast of Newfoundland","volume":"45","author":"Barber","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ulaby, F., and Long, D. (2015). Microwave Radar and Radiometric Remote Sensing, Artech House.","DOI":"10.3998\/0472119356"},{"key":"ref_5","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_6","unstructured":"World Meteorological Organization (1970). WMO Sea-Ice Nomenclature: Terminology, Codes, Illustrated Glossary and Symbols, Secretariat of the World Meteorological Organization."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2019.04.031","article-title":"Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter","volume":"229","author":"Howell","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3860","DOI":"10.1109\/TGRS.2017.2682859","article-title":"Diurnal scale controls on C-band microwave backscatter from snow-covered first-year sea ice during the transition from late winter to early melt","volume":"55","author":"Yackel","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3256","DOI":"10.1109\/TGRS.2010.2043954","article-title":"C-Band Polarimetric Backscattering Signatures of Newly Formed Sea Ice During Fall Freeze-Up","volume":"48","author":"Isleifson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3087","DOI":"10.1080\/014311699211633","article-title":"Unsupervised segmentation of ERS and radarsat sea ice images using multiresolution peak detection and aggregated population equalization","volume":"20","author":"Soh","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.5589\/m04-060","article-title":"Classification of fully polarimetric single- and dual-frequency SAR data of sea ice using the Wishart statistics","volume":"31","author":"Scheuchl","year":"2005","journal-title":"Can. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/15481603.2015.1026050","article-title":"Landfast sea ice monitoring using multisensor fusion in the Antarctic","volume":"52","author":"Kim","year":"2015","journal-title":"GISci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1109\/TGRS.2019.2892723","article-title":"Estimating Sea Ice Concentration From SAR: Training Convolutional Neural Networks With Passive Microwave Data","volume":"57","author":"Cooke","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"C02S03","DOI":"10.1029\/2005JC003384","article-title":"Sea ice remote sensing using AMSR-E 89-GHz channels","volume":"113","author":"Spreen","year":"2008","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TGRS.2016.2543660","article-title":"Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1109\/TGRS.2004.828179","article-title":"Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks","volume":"42","author":"Karvonen","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1525\/elementa.130","article-title":"Climate change and sea ice: Shipping accessibility on the marine transportation corridor through Hudson Bay and Hudson Strait (1980\u20132014)","volume":"5","author":"Andrews","year":"2017","journal-title":"Elem. Sci. Anthr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0422-9894(08)70900-0","article-title":"Coastal features of Canadian inland seas","volume":"Volume 44","author":"Martini","year":"1986","journal-title":"Elsevier Oceanography Series"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1657\/1938-4246-46.1.66","article-title":"An update on the ice climatology of the Hudson Bay system","volume":"46","author":"Hochheim","year":"2014","journal-title":"Arctic Antarct. Alp. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1525\/elementa.412","article-title":"Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay","volume":"8","author":"Harasyn","year":"2020","journal-title":"Elem. Sci. Anthr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20837","DOI":"10.1029\/1999JC900082","article-title":"Arctic sea ice extents, areas, and trends, 1978\u20131996","volume":"104","author":"Parkinson","year":"1999","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_24","unstructured":"Slade, B. (2020, July 31). RADARSAT-2 Product Description. RN-SP-52-1238 2018. Available online: https:\/\/mdacorporation.com\/docs\/default-source\/technical-documents\/geospatial-services\/52-1238_rs2_product_description.pdf."},{"key":"ref_25","unstructured":"MANICE, CIS (2005). Manual of Standard Procedures for Observing and Reporting Ice Conditions, Canadian Ice Service (CIS), Meteorological Service of Canada. [9th ed.]."},{"key":"ref_26","unstructured":"MacDonald, D. Ltd. (2008). Radarsat-2 Product Format Definition, MDA Corporation. Available online: https:\/\/mdacorporation.com\/docs\/default-source\/technical-documents\/geospatial-services\/radarsat-2-product-format-definition.pdf?sfvrsn=4."},{"key":"ref_27","unstructured":"MacDonald, D. Ltd. (2010). Geolocation of RADARSAT-2 Georeferenced Products, MDA Corporation. Available online: https:\/\/mdacorporation.com\/geospatial\/international\/satellites\/RADARSAT-2\/docs\/default-source\/technical-documents\/geospatial-services\/rn-tn-53-0076-geolocation-tn-1-3.pdf?sfvrsn=4."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5\u20139 October 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., and Jagersand, M. (2018). RTSeg: Real-time Semantic Segmentation Comparative Study. arXiv.","DOI":"10.1109\/ICIP.2018.8451495"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Singh, A., Kalke, H., Loewen, M., and Ray, N. (2019). River Ice Segmentation with Deep Learning. arXiv.","DOI":"10.1109\/TGRS.2020.2981082"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., and Kennedy, P.J. (2016, January 24\u201329). Training deep neural networks on imbalanced datasets. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727770"},{"key":"ref_32","unstructured":"Pleiss, G., Chen, D., Huang, G., Li, T., van der Maaten, L., and Weinberger, K.Q. (2017). Memory-Efficient Implementation of DenseNets. arXiv."},{"key":"ref_33","unstructured":"Masters, D., and Luschi, C. (2018). Revisiting Small Batch Training for Deep Neural Networks. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2486\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:53:55Z","timestamp":1760176435000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,3]]},"references-count":33,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12152486"],"URL":"https:\/\/doi.org\/10.3390\/rs12152486","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,3]]}}}