Articles | Volume 12, issue 4
https://doi.org/10.5194/tc-12-1307-2018
© Author(s) 2018. 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-12-1307-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2018
Research article |
| 12 Apr 2018
| 12 Apr 2018
Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery
Nicholas C. Wright
CORRESPONDING AUTHOR
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
Chris M. Polashenski
Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
U.S. Army Cold Regions Research and Engineering Laboratories, Hanover,
NH, USA
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Cited
27 citations as recorded by crossref.
- Relationships between summertime surface albedo and melt pond fraction in the central Arctic Ocean: The aggregate scale of albedo obtained on the MOSAiC floe R. Calmer et al. 10.1525/elementa.2023.00001
- Contrasting Sea‐Ice Algae Blooms in a Changing Arctic Documented by Autonomous Drifting Buoys V. Hill et al. 10.1029/2021JC017848
- Spatiotemporal evolution of melt ponds on Arctic sea ice M. Webster et al. 10.1525/elementa.2021.000072
- Pan-Arctic melt pond fraction trend, variability, and contribution to sea ice changes J. Feng et al. 10.1016/j.gloplacha.2022.103932
- Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery M. König et al. 10.3390/rs12162623
- Estimated Heat Budget During Summer Melt of Arctic First‐Year Sea Ice E. Skyllingstad & C. Polashenski 10.1029/2018GL080349
- SegIceNet: Activation Information Guided PointFlow for Sea Ice Segmentation Z. Wang et al. 10.1109/LGRS.2024.3384411
- The Scientific Legacy of NASA’s Operation IceBridge J. MacGregor et al. 10.1029/2020RG000712
- Can a computer see what an ice expert sees? Multilabel ice objects classification with convolutional neural networks E. Kim et al. 10.1016/j.rineng.2019.100036
- Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter Z. Peng et al. 10.3390/rs14184538
- Spatiotemporal Analysis of Sea Ice Leads in the Arctic Ocean Retrieved from IceBridge Laxon Line Data 2012–2018 D. Sha et al. 10.3390/rs13204177
- Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs B. Gonçalves & H. Lynch 10.3390/rs13183562
- Integrating a data-driven classifier and shape-modulated segmentation for sea-ice floe extraction A. Wang et al. 10.1016/j.jag.2024.103726
- A Multi-Sensor and Modeling Approach for Mapping Light Under Sea Ice During the Ice-Growth Season J. Stroeve et al. 10.3389/fmars.2020.592337
- Characteristics of sea ice kinematics from the marginal ice zone to the packed ice zone observed by buoys deployed during the 9th Chinese Arctic Expedition X. Chang et al. 10.1007/s13131-022-1990-8
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
- 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
- MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice I. Sudakow et al. 10.1109/JSTARS.2022.3213192
- Observations of sea ice melt from Operation IceBridge imagery N. Wright et al. 10.5194/tc-14-3523-2020
- Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network Y. Ding et al. 10.3390/rs12172746
- Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization P. Srivastava et al. 10.5194/acp-22-4763-2022
- A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
- Estimating Sea Ice Concentration From Microwave Radiometric Data for Arctic Summer Conditions Using Machine Learning X. Li & C. Xiong 10.1109/TGRS.2024.3382756
- Helicopter-borne RGB orthomosaics and photogrammetric digital elevation models from the MOSAiC Expedition N. Neckel et al. 10.1038/s41597-023-02318-5
- Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control T. Li et al. 10.3390/rs11070784
- Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture S. Li et al. 10.1088/1742-6596/2718/1/012062
- Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
27 citations as recorded by crossref.
- Relationships between summertime surface albedo and melt pond fraction in the central Arctic Ocean: The aggregate scale of albedo obtained on the MOSAiC floe R. Calmer et al. 10.1525/elementa.2023.00001
- Contrasting Sea‐Ice Algae Blooms in a Changing Arctic Documented by Autonomous Drifting Buoys V. Hill et al. 10.1029/2021JC017848
- Spatiotemporal evolution of melt ponds on Arctic sea ice M. Webster et al. 10.1525/elementa.2021.000072
- Pan-Arctic melt pond fraction trend, variability, and contribution to sea ice changes J. Feng et al. 10.1016/j.gloplacha.2022.103932
- Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery M. König et al. 10.3390/rs12162623
- Estimated Heat Budget During Summer Melt of Arctic First‐Year Sea Ice E. Skyllingstad & C. Polashenski 10.1029/2018GL080349
- SegIceNet: Activation Information Guided PointFlow for Sea Ice Segmentation Z. Wang et al. 10.1109/LGRS.2024.3384411
- The Scientific Legacy of NASA’s Operation IceBridge J. MacGregor et al. 10.1029/2020RG000712
- Can a computer see what an ice expert sees? Multilabel ice objects classification with convolutional neural networks E. Kim et al. 10.1016/j.rineng.2019.100036
- Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter Z. Peng et al. 10.3390/rs14184538
- Spatiotemporal Analysis of Sea Ice Leads in the Arctic Ocean Retrieved from IceBridge Laxon Line Data 2012–2018 D. Sha et al. 10.3390/rs13204177
- Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs B. Gonçalves & H. Lynch 10.3390/rs13183562
- Integrating a data-driven classifier and shape-modulated segmentation for sea-ice floe extraction A. Wang et al. 10.1016/j.jag.2024.103726
- A Multi-Sensor and Modeling Approach for Mapping Light Under Sea Ice During the Ice-Growth Season J. Stroeve et al. 10.3389/fmars.2020.592337
- Characteristics of sea ice kinematics from the marginal ice zone to the packed ice zone observed by buoys deployed during the 9th Chinese Arctic Expedition X. Chang et al. 10.1007/s13131-022-1990-8
- Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery S. Lee et al. 10.1016/j.rse.2020.111919
- 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
- MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice I. Sudakow et al. 10.1109/JSTARS.2022.3213192
- Observations of sea ice melt from Operation IceBridge imagery N. Wright et al. 10.5194/tc-14-3523-2020
- Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network Y. Ding et al. 10.3390/rs12172746
- Ship-based estimates of momentum transfer coefficient over sea ice and recommendations for its parameterization P. Srivastava et al. 10.5194/acp-22-4763-2022
- A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
- Estimating Sea Ice Concentration From Microwave Radiometric Data for Arctic Summer Conditions Using Machine Learning X. Li & C. Xiong 10.1109/TGRS.2024.3382756
- Helicopter-borne RGB orthomosaics and photogrammetric digital elevation models from the MOSAiC Expedition N. Neckel et al. 10.1038/s41597-023-02318-5
- Resolving Fine-Scale Surface Features on Polar Sea Ice: A First Assessment of UAS Photogrammetry Without Ground Control T. Li et al. 10.3390/rs11070784
- Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture S. Li et al. 10.1088/1742-6596/2718/1/012062
- Copernicus Ocean State Report, issue 6 10.1080/1755876X.2022.2095169
Latest update: 24 Apr 2024
Short summary
Satellites, planes, and drones capture thousands of images of the Arctic sea ice cover each year. However, few methods exist to reliably and automatically process these images for scientifically usable information. In this paper, we take the next step towards a community standard for analyzing these images by presenting an open-source platform able to accurately classify sea ice imagery into several important surface types.
Satellites, planes, and drones capture thousands of images of the Arctic sea ice cover each...
