Articles | Volume 14, issue 8
https://doi.org/10.5194/tc-14-2629-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-2629-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Aug 2020
Research article |
| 20 Aug 2020
| 20 Aug 2020
Classification of sea ice types in Sentinel-1 synthetic aperture radar images
Jeong-Won Park
CORRESPONDING AUTHOR
Ocean and Sea Ice Remote Sensing Group, Nansen Environmental and
Remote Sensing Center, 5006 Bergen, Norway
Unit of Arctic Sea Ice Prediction, Korea Polar Research Institute,
Incheon, 21990, South Korea
Anton Andreevich Korosov
Ocean and Sea Ice Remote Sensing Group, Nansen Environmental and
Remote Sensing Center, 5006 Bergen, Norway
Mohamed Babiker
Ocean and Sea Ice Remote Sensing Group, Nansen Environmental and
Remote Sensing Center, 5006 Bergen, Norway
Joong-Sun Won
Department of Earth System Sciences, Yonsei University, Seoul, 03722,
South Korea
Morten Wergeland Hansen
Ocean and Sea Ice Remote Sensing Group, Nansen Environmental and
Remote Sensing Center, 5006 Bergen, Norway
Department of Remote Sensing and Data Management, Norwegian
Meteorological Institute, 0371 Oslo, Norway
Hyun-Cheol Kim
Unit of Arctic Sea Ice Prediction, Korea Polar Research Institute,
Incheon, 21990, South Korea
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- A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al. 10.1109/JSTARS.2022.3194324
- Synthetic Aperture Radar (SAR) for Ocean: A Review R. Asiyabi et al. 10.1109/JSTARS.2023.3310363
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- A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season Y. Ren et al. 10.1109/TGRS.2022.3177600
- Arctic Sea Ice and Open Water Classification From Spaceborne Fully Polarimetric Synthetic Aperture Radar Y. Lu et al. 10.1109/TGRS.2023.3266158
- Partial Label Learning With Focal Loss for Sea Ice Classification Based on Ice Charts B. Vahedi et al. 10.1109/JSTARS.2024.3413003
- Spatio-temporal distribution of sea-ice thickness using a machine learning approach with Google Earth Engine and Sentinel-1 GRD data R. Shamshiri et al. 10.1016/j.rse.2021.112851
- Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images Y. Ren et al. 10.1109/LGRS.2021.3058049
- Analyzing short term spatial and temporal dynamics of water presence at a basin-scale in Mexico using SAR data A. López-Caloca et al. 10.1080/15481603.2020.1840106
- A Multiscale Dual Attention Network for the Automatic Classification of Polar Sea Ice and Open Water Based on Sentinel-1 SAR Images Z. Zhang et al. 10.1109/JSTARS.2024.3354912
- Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification E. Khachatrian et al. 10.1109/JSTARS.2021.3099398
- Incidence Angle Dependencies for C-Band Backscatter From Sea Ice During Both the Winter and Melt Season T. Geldsetzer & S. Howell 10.1109/TGRS.2023.3315056
- Incidence angle dependency and seasonal evolution of L and C-band SAR backscatter over landfast sea ice T. Karlsen et al. 10.1017/aog.2024.30
- Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F T. Williams et al. 10.5194/tc-15-3207-2021
- Classification of optical water groups in the subarctic pacific and adjacent seas using satellite-derived light absorption spectra of chromophoric dissolved organic matter J. Oida et al. 10.1016/j.dsr.2024.104313
- Recent Developments in Artificial Intelligence in Oceanography C. Dong et al. 10.34133/2022/9870950
- Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery F. Müller et al. 10.5194/tc-17-809-2023
- Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized SAR Sea Ice Imagery X. Chen et al. 10.1109/TGRS.2022.3233871
- PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction D. Zhang et al. 10.3390/app14083278
- Robust Multiseasonal Ice Classification From High-Resolution X-Band SAR K. Kortum et al. 10.1109/TGRS.2022.3144731
- Calibration of sea ice drift forecasts using random forest algorithms C. Palerme & M. Müller 10.5194/tc-15-3989-2021
- Automatic Detection of Low-Backscatter Targets in the Arctic Using Wide Swath Sentinel-1 Imagery A. Cristea et al. 10.1109/JSTARS.2022.3214069
- Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network H. Wan et al. 10.3390/rs15164014
- Dynamic Thresholding Fully Automated sea ice extraction and classification methods based on multi-source remote-sensing data in the Yellow sea and Bohai sea regions J. Xu et al. 10.1016/j.asr.2024.05.073
- Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data L. He et al. 10.1109/JSTARS.2022.3170732
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- Delineating Polynya Area Using Active and Passive Microwave Sensors for the Western Ross Sea Sector of Antarctica G. Burada et al. 10.3390/rs15102545
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- Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery Y. Huang et al. 10.1016/j.rse.2024.114204
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- A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade H. Lyu et al. 10.1109/JSTARS.2022.3194324
- Synthetic Aperture Radar (SAR) for Ocean: A Review R. Asiyabi et al. 10.1109/JSTARS.2023.3310363
4 citations as recorded by crossref.
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- Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning Y. Han et al. 10.3390/rs13040592
- Changes Detection of Ice Dimension in Cheonji, Baekdu Mountain Using Sentinel-1 Image Classification S. Park et al. 10.5467/JKESS.2020.41.1.31
- Isometric mapping algorithm based GNSS-R sea ice detection Y. Hu et al. 10.3934/mina.2024002
Latest update: 19 Nov 2024
Short summary
A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.
A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the...
