Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5519-2023
© Author(s) 2023. 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-17-5519-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Dec 2023
Research article |
| 22 Dec 2023
| 22 Dec 2023
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Qin Zhang
CORRESPONDING AUTHOR
Norwegian Ice Service, Norwegian Meteorological Institute, Kirkegårdsveien 60, P.O. Box 6314 Langnes, 9293 Tromsø, Norway
Nick Hughes
Norwegian Ice Service, Norwegian Meteorological Institute, Kirkegårdsveien 60, P.O. Box 6314 Langnes, 9293 Tromsø, Norway
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Short summary
To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
To alleviate tedious manual image annotations for training deep learning (DL) models in floe...
