Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5519-2023
https://doi.org/10.5194/tc-17-5519-2023
Research article
 | 
22 Dec 2023
Research article |  | 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 and Nick Hughes

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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.