{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T10:53:22Z","timestamp":1780656802946,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"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":["41976214"],"award-info":[{"award-number":["41976214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1407100"],"award-info":[{"award-number":["2018YFC1407100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, the performance of C-band synthetic aperture radar (SAR) Gaofen-3 (GF-3) quad-polarization Stripmap (QPS) data is assessed for classifying late spring and summer sea ice types. The investigation is based on 18 scenes of GF-3 QPS data acquired in the Arctic Ocean in 2017. In this study, floe ice (FI), brash ice (BI) between floes and open water (OW, ice-free area) were classified based on a mini sea ice residual convolutional network, which we call MSI-ResNet. While investigating the optimal patch size for MSI-ResNet, we found that, as the patch size continues to grow, the classification accuracy first increases and then decreases. A patch size of 31 \u00d7 31 was found to achieve the best performance. The performance of classification using different polarization combinations from the QPS data was also assessed. The vertical-vertical (VV) polarization input overestimates the FI category while incorrectly identifying most of the BI as FI. The VH polarization produces a synchronous improvement in FI, BI, and OW discrimination, with a higher overall accuracy and kappa coefficient (91.09% and 0.85, respectively) than the VV polarization (83.37% and 0.70, respectively). The combination of VV and vertical-horizontal (VH) polarizations presents a modest precision improvement for BI and OW together with a slight overestimation for FI. With VV, VH, and horizontal-horizontal (HH) polarization data as the inputs, the user\u2019s accuracy improves to 95.12%, 93.42%, and 95.17% for FI, BI, and OW, respectively. The accuracy was assessed against visual interpretation of the sea ice classes in the images using a stratified sampling method. The application of the MSI-ResNet method to data covering the Beaufort Sea and the north of the Severnaya Zemlya archipelago was found to achieve a high overall accuracy (kappa) of 94.62% (\u00b10.92) and 94.23% (\u00b10.90), respectively. This is similar to the classification accuracy obtained in the Fram Strait. From the results of this study, it is shown that the MSI-ResNet method performs better than the classical support vector machine (SVM) classifier for sea ice discrimination. The GF-3 QPS mode data also show more details in discriminating scattered sea ice floes than the coincident Sentinel-1A Extra Wide (EW) swath mode data.<\/jats:p>","DOI":"10.3390\/rs13081452","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T05:52:00Z","timestamp":1618206720000},"page":"1452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-5719","authenticated-orcid":false,"given":"Tianyu","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"},{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University &amp; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China"},{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University &amp; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Shokr","sequence":"additional","affiliation":[{"name":"Science and Technology Branch, Environment and Climate Change Canada, Toronto, ON M3H5T4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunlei","family":"Mi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5009-5413","authenticated-orcid":false,"given":"Xiao-Ming","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hainan Key Laboratory of Earth Observation, Sanya 572029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University &amp; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengming","family":"Hui","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University &amp; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1080\/07038992.1992.10855141","article-title":"Science Issues Relating to Marine Aspects of the Cryosphere: Implications for Remote Sensing","volume":"18","author":"Barber","year":"1992","journal-title":"Can. 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