Articles | Volume 11, issue 1
https://doi.org/10.5194/tc-11-33-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-11-33-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Jan 2017
Research article |
| 11 Jan 2017
Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images
Natalia Zakhvatkina
CORRESPONDING AUTHOR
Nansen International Environmental and Remote Sensing Centre (Nansen
Centre, NIERSC), 14th Line 7, Office 49, Vasilievsky Island, St. Petersburg,
199034, Russian Federation
Arctic and Antarctic Research Institute (AARI), Bering Str. 38, St.
Petersburg, 199397, Russian Federation
Anton Korosov
Nansen Environmental and Remote Sensing Center (NERSC),
Thormøhlensgate 47, 5006 Bergen, Norway
Stefan Muckenhuber
Nansen Environmental and Remote Sensing Center (NERSC),
Thormøhlensgate 47, 5006 Bergen, Norway
Stein Sandven
Nansen Environmental and Remote Sensing Center (NERSC),
Thormøhlensgate 47, 5006 Bergen, Norway
Mohamed Babiker
Nansen Environmental and Remote Sensing Center (NERSC),
Thormøhlensgate 47, 5006 Bergen, Norway
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Cited
86 citations as recorded by crossref.
- Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery J. Wang et al. https://doi.org/10.3390/rs10111727
- Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada B. Montpetit et al. https://doi.org/10.1080/07038992.2023.2247091
- Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel J. Park et al. https://doi.org/10.1109/TGRS.2017.2765248
- Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023 Z. Swirad et al. https://doi.org/10.5194/tc-18-895-2024
- Review of Methods to Retrieve Sea-Ice Parameters from Satellite Microwave Radiometer Data E. Zabolotskikh https://doi.org/10.1134/S0001433818060166
- SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay W. Song et al. https://doi.org/10.3390/rs14010168
- Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data S. Kern et al. https://doi.org/10.5194/tc-16-349-2022
- Icy lakes extraction and water-ice classification using Landsat 8 OLI multispectral data K. Barbieux et al. https://doi.org/10.1080/01431161.2018.1447165
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al. https://doi.org/10.1109/JSTARS.2025.3551976
- Spatiotemporal Evolution Monitoring of Small Water Body Coverage Associated with Land Subsidence Using SAR Data: A Case Study in Geleshan, Chongqing, China T. Jiang et al. https://doi.org/10.3390/rs18040644
- Support vector machine learning applied to digital images of river ice conditions H. Kalke & M. Loewen https://doi.org/10.1016/j.coldregions.2018.08.014
- BAFormer: boundary-aware transformer for sea ice leads extraction from Sentinel-1 SAR images B. He et al. https://doi.org/10.1080/17538947.2026.2690734
- Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data X. Li et al. https://doi.org/10.1109/TGRS.2020.3007789
- A review of artificial intelligence in marine science T. Song et al. https://doi.org/10.3389/feart.2023.1090185
- Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification J. Lohse et al. https://doi.org/10.3390/rs13040552
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. https://doi.org/10.1109/TGRS.2022.3233871
- Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features M. Jiang et al. https://doi.org/10.1109/JSTARS.2022.3205849
- E-MPSPNet: Ice–Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision W. Song et al. https://doi.org/10.3390/rs14225753
- Partial Label Learning With Focal Loss for Sea Ice Classification Based on Ice Charts B. Vahedi et al. https://doi.org/10.1109/JSTARS.2024.3413003
- Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood P. Nakmuenwai et al. https://doi.org/10.3390/rs9010078
- Light from space illuminating the polar silk road X. Li et al. https://doi.org/10.1080/17538947.2022.2139865
- Observations from C-Band SAR Fully Polarimetric Parameters of Mobile Sea Ice Based on Radar Scattering Mechanisms to Support Operational Sea Ice Monitoring M. Shokr et al. https://doi.org/10.1080/07038992.2021.2003701
- Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements S. Singha et al. https://doi.org/10.1109/TGRS.2018.2809504
- Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images A. Cristea et al. https://doi.org/10.1109/JSTARS.2020.2993067
- Evaluation of the AMSR2 Ice Extent at the Arctic Sea Ice Edge Using an SAR-Based Ice Extent Product Y. Sun et al. https://doi.org/10.1109/TGRS.2023.3281594
- Automated Detection of Ice and Open Water From Dual-Polarization RADARSAT-2 Images for Data Assimilation A. Komarov & M. Buehner https://doi.org/10.1109/TGRS.2017.2713987
- IceGCN: An Interactive Sea Ice Classification Pipeline for SAR Imagery Based on Graph Convolutional Network M. Jiang et al. https://doi.org/10.3390/rs16132301
- An Arctic sea ice multi-step classification based on GNSS-R data from the TDS-1 mission N. Rodriguez-Alvarez et al. https://doi.org/10.1016/j.rse.2019.05.021
- Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel Images J. Park et al. https://doi.org/10.1109/TGRS.2018.2889381
- Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images A. Komarov & M. Buehner https://doi.org/10.1109/LGRS.2018.2806189
- MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery S. Chen et al. https://doi.org/10.3390/rs12193221
- Pan-Arctic sea ice drift data at 400 m grids based on spaceborne SAR Y. Qiu & X. Li https://doi.org/10.1038/s41597-026-06683-9
- Retrieval of Sea Ice Drift From the Central Arctic to the Fram Strait Based on Sequential Sentinel-1 SAR Data Y. Qiu & X. Li https://doi.org/10.1109/TGRS.2022.3226223
- Satellite SAR Data-based Sea Ice Classification: An Overview N. Zakhvatkina et al. https://doi.org/10.3390/geosciences9040152
- Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR H. Han et al. https://doi.org/10.1080/15481603.2020.1767857
- Evaluation of a Neural Network With Uncertainty for Detection of Ice and Water in SAR Imagery N. Asadi et al. https://doi.org/10.1109/TGRS.2020.2992454
- X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring A. Johansson et al. https://doi.org/10.1016/j.rse.2017.10.032
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al. https://doi.org/10.1016/j.rsase.2025.101538
- Automated Sentinel-1 sea ice type mapping and in-situ validation during the CIRFA-22 cruise J. Lohse et al. https://doi.org/10.1017/aog.2024.23
- Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data A. Wu et al. https://doi.org/10.1080/17538947.2022.2126016
- Deep-Learning-Based Sea Ice Classification With Sentinel-1 and AMSR-2 Data L. Zhao et al. https://doi.org/10.1109/JSTARS.2023.3285857
- Classification of sea ice types in Sentinel-1 synthetic aperture radar images J. Park et al. https://doi.org/10.5194/tc-14-2629-2020
- Observations of SAR polarimetric parameters of lake and fast sea ice during the early growth phase M. Shokr & M. Dabboor https://doi.org/10.1016/j.rse.2020.111910
- Discrimination of open water from sea ice in the Labrador Sea using quad-polarized synthetic aperture radar T. Xie et al. https://doi.org/10.1016/j.rse.2020.111948
- Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks H. Boulze et al. https://doi.org/10.3390/rs12132165
- Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning Y. Wang & X. Li https://doi.org/10.5194/essd-13-2723-2021
- Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine M. Qi et al. https://doi.org/10.3390/rs12142217
- Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle J. Lohse et al. https://doi.org/10.1017/aog.2020.45
- MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data M. Mäkynen & J. Karvonen https://doi.org/10.3390/rs9121324
- Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs A. Chiodi et al. https://doi.org/10.3389/fmars.2021.640697
- Arctic Sea Ice and Open Water Classification From Dual-Polarization Synthetic Aperture Radar Imagery and Deep Learning Models Y. Lu et al. https://doi.org/10.1109/JSTARS.2025.3564847
- Antarctic Sea ice distribution detection based on improved ant colony algorithm X. Wang & Z. Sun https://doi.org/10.3389/fmars.2024.1500537
- First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea W. Aldenhoff et al. https://doi.org/10.1109/JSTARS.2020.2977506
- Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images Over Sea Ice Y. Sun & X. Li https://doi.org/10.1109/TGRS.2020.3005831
- Automatic discrimination approach of sea ice in the Arctic Ocean using Sentinel-1 Extra Wide Swath dual-polarized SAR data D. Hong & C. Yang https://doi.org/10.1080/01431161.2017.1415486
- A Spatio-Temporal Deep Learning Model for Automatic Arctic Sea Ice Classification with Sentinel-1 SAR Imagery L. Zhao et al. https://doi.org/10.3390/rs17020277
- Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions R. Pires de Lima et al. https://doi.org/10.1080/01431161.2023.2248560
- Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT–2 SAR Images and CNNs N. Saberi et al. https://doi.org/10.3390/rs14030644
- Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images J. Hwang & H. Jung https://doi.org/10.3390/rs10111799
- An Operational Tool for the Automatic Detection and Removal of Border Noise in Sentinel-1 GRD Products M. Stasolla & X. Neyt https://doi.org/10.3390/s18103454
- Ice Floe Tracker: An algorithm to automatically retrieve Lagrangian trajectories via feature matching from moderate-resolution visual imagery R. Lopez-Acosta et al. https://doi.org/10.1016/j.rse.2019.111406
- Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery W. Aldenhoff et al. https://doi.org/10.1017/aog.2018.7
- Comparative Evaluation of Sea Ice Lead Detection Based on SAR Imagery and Altimeter Data N. Longepe et al. https://doi.org/10.1109/TGRS.2018.2889519
- A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery B. Wang et al. https://doi.org/10.3390/rs13193945
- TanDEM-X multiparametric data features in sea ice classification over the Baltic sea M. Marbouti et al. https://doi.org/10.1080/10095020.2020.1845574
- Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal Features Learning W. Song et al. https://doi.org/10.1109/TGRS.2020.3049031
- A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion D. Malmgren-Hansen et al. https://doi.org/10.1109/TGRS.2020.3004539
- Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics M. Qu et al. https://doi.org/10.1016/j.rse.2024.114193
- Can a computer see what an ice expert sees? Multilabel ice objects classification with convolutional neural networks E. Kim et al. https://doi.org/10.1016/j.rineng.2019.100036
- Method for detection of leads from Sentinel-1 SAR images D. Murashkin et al. https://doi.org/10.1017/aog.2018.6
- Improved Retrieval of Ice and Open Water From Sequential RADARSAT-2 Images A. Komarov & M. Buehner https://doi.org/10.1109/TGRS.2018.2886685
- Arctic Sea Ice and Open Water Classification From Spaceborne Fully Polarimetric Synthetic Aperture Radar Y. Lu et al. https://doi.org/10.1109/TGRS.2023.3266158
- Data Augmentation for SAR Sea Ice and Water Classification Based on Per-Class Backscatter Variation With Incidence Angle Q. Wang et al. https://doi.org/10.1109/TGRS.2023.3291927
- Arctic Wintertime Sea Ice Lead Detection From Sentinel-1 SAR Images S. Chen et al. https://doi.org/10.1109/TGRS.2024.3444045
- The AutoICE Challenge A. Stokholm et al. https://doi.org/10.5194/tc-18-3471-2024
- Gray Level Co-occurrence Matrix textural analysis for temporal mapping of sea ice in Sentinel-1A SAR images F. HILLEBRAND et al. https://doi.org/10.1590/0001-3765202420240554
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- Monitoring of Dangerous Ice Phenomena Using Satellite Imagery and Model Simulation V. Smirnov et al. https://doi.org/10.3103/S1068373919110049
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- Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network I. de Gelis et al. https://doi.org/10.1109/JSTARS.2021.3074068
- Sea Ice Detection from RADARSAT-2 Quad-Polarization SAR Imagery Based on Co- and Cross-Polarization Ratio L. Zhao et al. https://doi.org/10.3390/rs16030515
- Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges W. Huang et al. https://doi.org/10.3390/rs16050842
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- A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al. https://doi.org/10.1109/JSTARS.2022.3194324
86 citations as recorded by crossref.
- Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery J. Wang et al. https://doi.org/10.3390/rs10111727
- Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada B. Montpetit et al. https://doi.org/10.1080/07038992.2023.2247091
- Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel J. Park et al. https://doi.org/10.1109/TGRS.2017.2765248
- Extent, duration and timing of the sea ice cover in Hornsund, Svalbard, from 2014–2023 Z. Swirad et al. https://doi.org/10.5194/tc-18-895-2024
- Review of Methods to Retrieve Sea-Ice Parameters from Satellite Microwave Radiometer Data E. Zabolotskikh https://doi.org/10.1134/S0001433818060166
- SI-STSAR-7: A Large SAR Images Dataset with Spatial and Temporal Information for Classification of Winter Sea Ice in Hudson Bay W. Song et al. https://doi.org/10.3390/rs14010168
- Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data S. Kern et al. https://doi.org/10.5194/tc-16-349-2022
- Icy lakes extraction and water-ice classification using Landsat 8 OLI multispectral data K. Barbieux et al. https://doi.org/10.1080/01431161.2018.1447165
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al. https://doi.org/10.1109/JSTARS.2025.3551976
- Spatiotemporal Evolution Monitoring of Small Water Body Coverage Associated with Land Subsidence Using SAR Data: A Case Study in Geleshan, Chongqing, China T. Jiang et al. https://doi.org/10.3390/rs18040644
- Support vector machine learning applied to digital images of river ice conditions H. Kalke & M. Loewen https://doi.org/10.1016/j.coldregions.2018.08.014
- BAFormer: boundary-aware transformer for sea ice leads extraction from Sentinel-1 SAR images B. He et al. https://doi.org/10.1080/17538947.2026.2690734
- Extraction of Sea Ice Cover by Sentinel-1 SAR Based on Support Vector Machine With Unsupervised Generation of Training Data X. Li et al. https://doi.org/10.1109/TGRS.2020.3007789
- A review of artificial intelligence in marine science T. Song et al. https://doi.org/10.3389/feart.2023.1090185
- Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification J. Lohse et al. https://doi.org/10.3390/rs13040552
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. https://doi.org/10.1109/TGRS.2022.3233871
- Sea-Ice Mapping of RADARSAT-2 Imagery by Integrating Spatial Contexture With Textural Features M. Jiang et al. https://doi.org/10.1109/JSTARS.2022.3205849
- E-MPSPNet: Ice–Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision W. Song et al. https://doi.org/10.3390/rs14225753
- Partial Label Learning With Focal Loss for Sea Ice Classification Based on Ice Charts B. Vahedi et al. https://doi.org/10.1109/JSTARS.2024.3413003
- Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood P. Nakmuenwai et al. https://doi.org/10.3390/rs9010078
- Light from space illuminating the polar silk road X. Li et al. https://doi.org/10.1080/17538947.2022.2139865
- Observations from C-Band SAR Fully Polarimetric Parameters of Mobile Sea Ice Based on Radar Scattering Mechanisms to Support Operational Sea Ice Monitoring M. Shokr et al. https://doi.org/10.1080/07038992.2021.2003701
- Arctic Sea Ice Characterization Using Spaceborne Fully Polarimetric L-, C-, and X-Band SAR With Validation by Airborne Measurements S. Singha et al. https://doi.org/10.1109/TGRS.2018.2809504
- Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images A. Cristea et al. https://doi.org/10.1109/JSTARS.2020.2993067
- Evaluation of the AMSR2 Ice Extent at the Arctic Sea Ice Edge Using an SAR-Based Ice Extent Product Y. Sun et al. https://doi.org/10.1109/TGRS.2023.3281594
- Automated Detection of Ice and Open Water From Dual-Polarization RADARSAT-2 Images for Data Assimilation A. Komarov & M. Buehner https://doi.org/10.1109/TGRS.2017.2713987
- IceGCN: An Interactive Sea Ice Classification Pipeline for SAR Imagery Based on Graph Convolutional Network M. Jiang et al. https://doi.org/10.3390/rs16132301
- An Arctic sea ice multi-step classification based on GNSS-R data from the TDS-1 mission N. Rodriguez-Alvarez et al. https://doi.org/10.1016/j.rse.2019.05.021
- Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel Images J. Park et al. https://doi.org/10.1109/TGRS.2018.2889381
- Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images A. Komarov & M. Buehner https://doi.org/10.1109/LGRS.2018.2806189
- MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery S. Chen et al. https://doi.org/10.3390/rs12193221
- Pan-Arctic sea ice drift data at 400 m grids based on spaceborne SAR Y. Qiu & X. Li https://doi.org/10.1038/s41597-026-06683-9
- Retrieval of Sea Ice Drift From the Central Arctic to the Fram Strait Based on Sequential Sentinel-1 SAR Data Y. Qiu & X. Li https://doi.org/10.1109/TGRS.2022.3226223
- Satellite SAR Data-based Sea Ice Classification: An Overview N. Zakhvatkina et al. https://doi.org/10.3390/geosciences9040152
- Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR H. Han et al. https://doi.org/10.1080/15481603.2020.1767857
- Evaluation of a Neural Network With Uncertainty for Detection of Ice and Water in SAR Imagery N. Asadi et al. https://doi.org/10.1109/TGRS.2020.2992454
- X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring A. Johansson et al. https://doi.org/10.1016/j.rse.2017.10.032
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al. https://doi.org/10.1016/j.rsase.2025.101538
- Automated Sentinel-1 sea ice type mapping and in-situ validation during the CIRFA-22 cruise J. Lohse et al. https://doi.org/10.1017/aog.2024.23
- Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data A. Wu et al. https://doi.org/10.1080/17538947.2022.2126016
- Deep-Learning-Based Sea Ice Classification With Sentinel-1 and AMSR-2 Data L. Zhao et al. https://doi.org/10.1109/JSTARS.2023.3285857
- Classification of sea ice types in Sentinel-1 synthetic aperture radar images J. Park et al. https://doi.org/10.5194/tc-14-2629-2020
- Observations of SAR polarimetric parameters of lake and fast sea ice during the early growth phase M. Shokr & M. Dabboor https://doi.org/10.1016/j.rse.2020.111910
- Discrimination of open water from sea ice in the Labrador Sea using quad-polarized synthetic aperture radar T. Xie et al. https://doi.org/10.1016/j.rse.2020.111948
- Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks H. Boulze et al. https://doi.org/10.3390/rs12132165
- Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning Y. Wang & X. Li https://doi.org/10.5194/essd-13-2723-2021
- Monitoring the Ice Phenology of Qinghai Lake from 1980 to 2018 Using Multisource Remote Sensing Data and Google Earth Engine M. Qi et al. https://doi.org/10.3390/rs12142217
- Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle J. Lohse et al. https://doi.org/10.1017/aog.2020.45
- MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data M. Mäkynen & J. Karvonen https://doi.org/10.3390/rs9121324
- Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs A. Chiodi et al. https://doi.org/10.3389/fmars.2021.640697
- Arctic Sea Ice and Open Water Classification From Dual-Polarization Synthetic Aperture Radar Imagery and Deep Learning Models Y. Lu et al. https://doi.org/10.1109/JSTARS.2025.3564847
- Antarctic Sea ice distribution detection based on improved ant colony algorithm X. Wang & Z. Sun https://doi.org/10.3389/fmars.2024.1500537
- First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea W. Aldenhoff et al. https://doi.org/10.1109/JSTARS.2020.2977506
- Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images Over Sea Ice Y. Sun & X. Li https://doi.org/10.1109/TGRS.2020.3005831
- Automatic discrimination approach of sea ice in the Arctic Ocean using Sentinel-1 Extra Wide Swath dual-polarized SAR data D. Hong & C. Yang https://doi.org/10.1080/01431161.2017.1415486
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Saved (final revised paper)
Latest update: 02 Jul 2026
Short summary
The presented fully automated algorithm distinguishes open water (rough/calm) and sea ice based on dual-polarized RS2 SAR images. Texture features are used for Support Vector Machines supervised image classification. The algorithm includes pre-processing and validation procedures. More than 2700 scenes were processed and the results show the good discrimination between open water and sea ice areas with accuracy 91 % compared with ice charts produced by MET Norway service.
The presented fully automated algorithm distinguishes open water (rough/calm) and sea ice based...
