Articles | Volume 12, issue 4
https://doi.org/10.5194/tc-12-1307-2018
https://doi.org/10.5194/tc-12-1307-2018
Research article
 | 
12 Apr 2018
Research article |  | 12 Apr 2018

Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery

Nicholas C. Wright and Chris M. Polashenski

Related authors

Observations of sea ice melt from Operation IceBridge imagery
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020,https://doi.org/10.5194/tc-14-3523-2020, 2020
Short summary

Related subject area

Discipline: Sea ice | Subject: Remote Sensing
Grounded ridge detection and characterization along the Alaska Arctic coastline using ICESat-2 surface height retrievals
Kennedy A. Lange, Alice C. Bradley, Kyle Duncan, and Sinéad L. Farrell
The Cryosphere, 19, 2045–2065, https://doi.org/10.5194/tc-19-2045-2025,https://doi.org/10.5194/tc-19-2045-2025, 2025
Short summary
Novel methods to study sea ice deformation, linear kinematic features and coherent dynamic clusters from imaging remote sensing data
Polona Itkin
The Cryosphere, 19, 1135–1151, https://doi.org/10.5194/tc-19-1135-2025,https://doi.org/10.5194/tc-19-1135-2025, 2025
Short summary
Drift-aware sea ice thickness maps from satellite remote sensing
Robert Ricker, Thomas Lavergne, Stefan Hendricks, Stephan Paul, Emily Down, Mari Anne Killie, and Marion Bocquet
EGUsphere, https://doi.org/10.5194/egusphere-2025-359,https://doi.org/10.5194/egusphere-2025-359, 2025
Short summary
Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
The Cryosphere, 19, 325–346, https://doi.org/10.5194/tc-19-325-2025,https://doi.org/10.5194/tc-19-325-2025, 2025
Short summary
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025,https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary

Cited articles

Arntsen, A. E., Song, A. J., Perovich, D. K., and Richter-Menge, J. A.: Observations of the summer breakup of an Arctic sea ice cover, Geophys. Res. Lett., 42, 8057–8063, https://doi.org/10.1002/2015GL065224, 2015.
Blaschke, T.: Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens., 65, 2–16, https://doi.org/10.1016/j.isprsjprs.2009.06.004, 2010.
Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F., and Tiede, D.: Geographic Object-Based Image Analysis – Towards a new paradigm, ISPRS J. Photogramm. Remote Sens., 87, 180–191, https://doi.org/10.1016/j.isprsjprs.2013.09.014, 2014.
Breiman, L.: Bagging Predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1023/A:1018054314350, 1996.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Download
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.
Share