48 citations as recorded by crossref.

1. Satellite SAR Data-based Sea Ice Classification: An Overview N. Zakhvatkina et al. https://doi.org/10.3390/geosciences9040152
2. 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
3. Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information H. Qiu et al. https://doi.org/10.3390/rs14040927
4. Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective M. Espeseth et al. https://doi.org/10.3390/rs9111088
5. The Dynamic Processes of Sea Ice on the East Coast of Antarctica—A Case Study Based on Spaceborne Synthetic Aperture Radar Data from TerraSAR-X H. Liu et al. https://doi.org/10.1109/JSTARS.2015.2497355
6. Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data A. Fors et al. https://doi.org/10.1109/JSTARS.2015.2504384
7. 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
8. Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data H. Lyu et al. https://doi.org/10.3390/rs14051165
9. Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band A. Fors et al. https://doi.org/10.5194/tc-10-401-2016
10. 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
11. Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning C. Baumhoer et al. https://doi.org/10.3390/rs11212529
12. Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning K. Radhakrishnan et al. https://doi.org/10.1109/JSTARS.2021.3076109
13. Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network H. Wan et al. https://doi.org/10.3390/rs15164014
14. Separating Sea Ice and Open Water in Sentinel-1 EW Imagery Based on a Model-Informed Threshold for Backscatter Intensity Decay Rates With Incidence Angle M. Ostersund et al. https://doi.org/10.1109/JSTARS.2026.3720699
15. Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar S. Cafarella et al. https://doi.org/10.1080/07038992.2019.1647102
16. Southern Ocean ice charts at the Argentine Naval Hydrographic Service and their impact on safety of navigation A. Scardilli et al. https://doi.org/10.3389/fmars.2022.971894
17. Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification J. Lohse et al. https://doi.org/10.3390/rs13040552
18. Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network L. Wang et al. https://doi.org/10.3390/rs9050408
19. Assessing polarimetric SAR sea-ice classifications using consecutive day images M. Moen et al. https://doi.org/10.3189/2015AoG69A802
20. Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images N. Zakhvatkina et al. https://doi.org/10.5194/tc-11-33-2017
21. Toward brash ice recognition for convoyed ship following: a segmentation framework combining segment-anything and morphological Chan-Vese models B. Mei et al. https://doi.org/10.1016/j.oceaneng.2026.127240
22. 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
23. Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data K. Blix et al. https://doi.org/10.1109/TGRS.2020.3022461
24. 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
25. Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar R. Ressel et al. https://doi.org/10.1109/JSTARS.2016.2539501
26. Analysis on the Effects of SAR Imaging Parameters and Environmental Conditions on the Standard Deviation of the Co-Polarized Phase Difference Measured over Sea Surface A. Buono et al. https://doi.org/10.3390/rs11010018
27. 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
28. 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
29. Evaluation of Modified Reflection Symmetry Decomposition Polarization Features for Sea Ice Classification T. Lan et al. https://doi.org/10.3390/rs17091584
30. An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery J. Lohse et al. https://doi.org/10.3390/rs11131574
31. 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. https://doi.org/10.1109/JSTARS.2024.3354912
32. Is Radar Phase Information Useful for Sea Ice Detection in the Marginal Ice Zone? F. Ding et al. https://doi.org/10.3390/rs12111847
33. The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX W. Copeland et al. https://doi.org/10.3389/fmars.2024.1400479
34. Arctic Sea Ice Characterization Using RISAT-1 Compact-Pol SAR Imagery and Feature Evaluation: A Case Study Over Northeast Greenland S. Singha & R. Ressel https://doi.org/10.1109/JSTARS.2017.2691258
35. Sea Ice and Water Segmentation in SAR Imagery Based on Polarization Channel Interaction and Edge Selective Fusion W. Song et al. https://doi.org/10.3390/rs18060945
36. Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data A. Gegiuc et al. https://doi.org/10.5194/tc-12-343-2018
37. Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery J. Wang et al. https://doi.org/10.3390/rs10111727
38. Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study L. Wang et al. https://doi.org/10.1109/TGRS.2016.2543660
39. Optimal Compact Polarimetric Parameters and Texture Features for Discriminating Sea Ice Types during Winter and Advanced Melt S. Nasonova et al. https://doi.org/10.1080/07038992.2018.1527683
40. ExtremeEarth Meets Satellite Data From Space D. Hagos et al. https://doi.org/10.1109/JSTARS.2021.3107982
41. Impact of Arctic sea ice floe-scale anisotropy on airborne electromagnetic surveys J. Negrel et al. https://doi.org/10.1017/aog.2020.61
42. Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks S. Khaleghian et al. https://doi.org/10.3390/rs13091734
43. 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
44. Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery A. Fors et al. https://doi.org/10.5194/tc-11-755-2017
45. Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity W. Guo et al. https://doi.org/10.5194/tc-16-237-2022
46. Combined observations of Arctic sea ice with near‐coincident colocated X‐band, C‐band, and L‐band SAR satellite remote sensing and helicopter‐borne measurements A. Johansson et al. https://doi.org/10.1002/2016JC012273
47. Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization M. Dabboor et al. https://doi.org/10.3390/rs10040594
48. 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