{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T07:22:44Z","timestamp":1778829764329,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2017-05049"],"award-info":[{"award-number":["RGPIN-2017-05049"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000016","name":"Canadian Space Agency","doi-asserted-by":"publisher","award":["SOAR program"],"award-info":[{"award-number":["SOAR program"]}],"id":[{"id":"10.13039\/501100000016","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently, the Canadian Ice Service (CIS) monitors lakes using synthetic aperture radar (SAR) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, we present an automatic ice-mapping approach which integrates unsupervised segmentation from the Iterative Region Growing using Semantics (IRGS) algorithm with supervised random forest (RF) labeling. IRGS first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. Recently, these output regions were manually labeled by the user to generate ice maps, or were labeled using a Support Vector Machine (SVM) classifier. Here, three labeling methods (Manual, SVM, and RF) are applied after IRGS segmentation to perform ice-water classification on 36 RADARSAT-2 scenes of Great Bear Lake (Canada). SVM and RF classifiers are also tested without integration with IRGS. An accuracy assessment has been performed on the results, comparing outcomes with author-generated reference data, as well as the reported ice fraction from CIS. The IRGS-RF average classification accuracy for this dataset is 95.8%, demonstrating the potential of this automated method to provide detailed and reliable lake ice cover information operationally.<\/jats:p>","DOI":"10.3390\/rs12091425","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T14:00:43Z","timestamp":1588600843000},"page":"1425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5490-5727","authenticated-orcid":false,"given":"Marie","family":"Hoekstra","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0603-8339","authenticated-orcid":false,"given":"Mingzhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-0875","authenticated-orcid":false,"given":"David A.","family":"Clausi","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1044-5850","authenticated-orcid":false,"given":"Claude","family":"Duguay","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L3G1, Canada"},{"name":"H2O Geomatics Inc., Waterloo, ON N2L1S7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s13280-011-0214-9","article-title":"Arctic freshwater ice and its climatic role","volume":"40","author":"Prowse","year":"2011","journal-title":"Ambio"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"869","DOI":"10.5194\/tc-5-869-2011","article-title":"The fate of lake ice in the North American Arctic","volume":"5","author":"Brown","year":"2011","journal-title":"Cryosphere"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hewitt, B., Lopez, L., Gaibisels, K., Murdoch, A., Higgins, S., Magnuson, J., Paterson, A., Rusak, J., Yao, H., and Sharma, S. (2018). Historical trends, drivers, and future projections of ice phenology in small north temperate lakes in the Laurentian Great Lakes Region. Water, 10.","DOI":"10.3390\/w10010070"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1126\/science.289.5485.1743","article-title":"Historical trends in lake and river ice cover in the Northern Hemisphere","volume":"289","author":"Magnuson","year":"2000","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/s41558-018-0393-5","article-title":"Widespread loss of lake ice around the Northern Hemisphere in a warming world","volume":"9","author":"Sharma","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1177\/0309133310375653","article-title":"The response and role of ice cover in lake-climate interactions","volume":"34","author":"Brown","year":"2010","journal-title":"Prog. Phys. Geogr."},{"key":"ref_7","unstructured":"Brown, R.D., Duguay, C.R., Goodison, B.E., Prowse, T.D., Ramsay, B., and Walker, A.E. (2002, January 2\u20136). Freshwater ice monitoring in Canada-an assessment of Canadian contributions for global climate monitoring. Proceedings of the 16th IAHR International Symposium on Ice, Dunedin, New Zealand."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"263","DOI":"10.5589\/m13-033","article-title":"Identification of polarimetric and nonpolarimetric C-band SAR parameters for application in the monitoring of lake ice freeze-up","volume":"39","author":"Geldsetzer","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.rse.2008.12.007","article-title":"Variability in ice phenology on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada, from SeaWinds\/QuikSCAT: 2000\u20132006","volume":"113","author":"Howell","year":"2009","journal-title":"Remote Sens. Environ."},{"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":"722","DOI":"10.3394\/0380-1330(2007)33[722:SSRSOG]2.0.CO;2","article-title":"Satellite SAR remote sensing of Great Lakes ice cover, Part 1. Ice backscatter signatures at C band","volume":"33","author":"Nghiem","year":"2007","journal-title":"J. Great Lakes Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1029\/GM068p0355","article-title":"An approach to identification of sea ice types from spaceborne SAR data","volume":"68","author":"Kwok","year":"1992","journal-title":"Microw. Remote Sens. Sea Ice"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3189\/2013AoG62A037","article-title":"Observing lake-and river-ice decay with SAR: Advantages and limitations of the unsupervised k-means classification approach","volume":"54","author":"Sobiech","year":"2013","journal-title":"Ann. Glaciol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1109\/TGRS.2007.908876","article-title":"SAR sea-ice image analysis based on iterative region growing using semantics","volume":"45","author":"Yu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2126","DOI":"10.1109\/TPAMI.2008.15","article-title":"IRGS: Image segmentation using edge penalties and region growing","volume":"30","author":"Yu","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","first-page":"28","article-title":"Automatic ice-ocean discrimination in SAR imagery","volume":"6","author":"Haarpaintner","year":"2007","journal-title":"Norut IT-Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/TGRS.2012.2212445","article-title":"Classification of sea ice types in ENVISAT synthetic aperture radar images","volume":"51","author":"Zakhvatkina","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","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":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zakhvatkina, N., Korosov, A., Muckenhuber, S., Sandven, S., and Babiker, M. (2017). Operational algorithm for ice\u2013water classification on dual-polarized RADARSAT-2 images. High Resolution Sea Ice Monitoring Using Space Borne Synthetic Aperture Radar, Copernicus Publications.","DOI":"10.5194\/tc-2016-131"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2017.1285501","article-title":"A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer","volume":"8","author":"Han","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2017.2743339","article-title":"Sea ice classification using Cryosat-2 altimeter data by optimal classifier\u2013feature assembly","volume":"14","author":"Shen","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/TGRS.2003.817819","article-title":"ARKTOS: An intelligent system for SAR sea ice image classification","volume":"42","author":"Soh","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"S13","DOI":"10.5589\/m10-008","article-title":"MAGIC: MAp-guided ice classification system","volume":"36","author":"Clausi","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_25","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":"GIScience Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Han, H., Im, J., Kim, M., Sim, S., Kim, J., Kim, D.j., and Kang, S.H. (2016). Retrieval of melt ponds on arctic multiyear sea ice in summer from terrasar-x dual-polarization data using machine learning approaches: A case study in the chukchi sea with mid-incidence angle data. Remote Sens., 8.","DOI":"10.3390\/rs8010057"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S391","DOI":"10.5589\/m11-001","article-title":"Monitoring lake ice during spring melt using RADARSAT-2 SAR","volume":"36","author":"Geldsetzer","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, H., Guo, H., Li, X.M., and Zhang, L. (2016, January 10\u201315). An approach to discrimination of sea ice from open water using SAR data. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730269"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4397","DOI":"10.1109\/TGRS.2012.2192278","article-title":"Operational SAR sea-ice image classification","volume":"50","author":"Ochilov","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, F., Clausi, D.A., Wang, L., and Xu, L. (2015, January 7\u201312). A semi-supervised approach for ice-water classification using dual-polarization SAR satellite imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301380"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., Duguay, C., Clausi, D., Pinard, V., and Howell, S. (2018). Semi-automated classification of lake ice cover using dual polarization RADARSAT-2 imagery. Remote Sens., 10.","DOI":"10.3390\/rs10111727"},{"key":"ref_33","unstructured":"MacDonald, D., and Associates Ltd. (2020, April 23). RADARSAT-2 Product Description. Available online: https:\/\/mdacorporation.com\/docs\/default-source\/technical-documents\/geospatial-services\/52-1238_rs2_product_description.pdf?sfvrsn=10."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/34.87344","article-title":"Watersheds in digital spaces: An efficient algorithm based on immersion simulations","volume":"6","author":"Vincent","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"352","DOI":"10.5589\/m12-028","article-title":"Feature extraction of dual-pol SAR imagery for sea ice image segmentation","volume":"38","author":"Yu","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.chemolab.2006.01.007","article-title":"Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products","volume":"83","author":"Granitto","year":"2006","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"288","DOI":"10.14445\/22315381\/IJETT-V15P255","article-title":"A survey of various machine learning techniques for text classification","volume":"15","author":"Chavan","year":"2014","journal-title":"Int. J. Eng. Trends Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1080\/00986445.2017.1292259","article-title":"Application and evaluation of random forest classifier technique for fault detection in bioreactor operation","volume":"204","author":"Shrivastava","year":"2017","journal-title":"Chem. Eng. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1002\/hyp.1026","article-title":"RADARSAT backscatter characteristics of ice growing on shallow sub-Arctic lakes, Churchill, Manitoba, Canada","volume":"16","author":"Duguay","year":"2002","journal-title":"Hydrol. Process."},{"key":"ref_40","unstructured":"Clausi, D.A. (2020, April 23). MAGIC System. Available online: https:\/\/uwaterloo.ca\/vision-image-processing-lab\/research-demos\/magic-system."},{"key":"ref_41","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:52:12Z","timestamp":1760363532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,30]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091425"],"URL":"https:\/\/doi.org\/10.3390\/rs12091425","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,30]]}}}