Mapping the extent of giant Antarctic icebergs with deep learning
-
Published:2023-11-09
Issue:11
Volume:17
Page:4675-4690
-
ISSN:1994-0424
-
Container-title:The Cryosphere
-
language:en
-
Short-container-title:The Cryosphere
Author:
Braakmann-Folgmann AnneORCID, Shepherd Andrew, Hogg DavidORCID, Redmond Ella
Abstract
Abstract. Icebergs release cold, fresh meltwater and terrigenous nutrients as they drift and melt, influencing the local ocean properties, encouraging sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, changes in their area and thickness have to be monitored along their trajectories. While the locations of large icebergs are operationally tracked by manual inspection, delineation of their extent is not. Here, we propose a U-net approach to automatically map the extent of giant icebergs in Sentinel-1 imagery. This greatly improves the efficiency compared to manual delineations, reducing the time for each outline from several minutes to less than 0.01 s. We evaluate the performance of our U-net and two state-of-the-art segmentation algorithms (Otsu and k-means) on 191 images. For icebergs larger than those covered by the training data, we find that U-net tends to miss parts. Otherwise, U-net is more robust in scenes with complex backgrounds – ignoring sea ice, smaller regions of nearby coast or other icebergs – and outperforms the other two techniques by achieving an F1 score of 0.84 and an absolute median deviation in iceberg area of 4.1 %.
Funder
Natural Environment Research Council
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
Reference62 articles.
1. Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R. and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nat. Commun., 12, 1–12, https://doi.org/10.1038/s41467-021-25257-4, 2021. 2. Barbat, M. M., Wesche, C., Werhli, A. V., and Mata, M. M.: An adaptive machine learning approach to improve automatic iceberg detection from SAR images, ISPRS J. Photogramm., 156, 247–259, https://doi.org/10.1016/j.isprsjprs.2019.08.015, 2019a. 3. Barbat, M. M., Rackow, T., Hellmer, H. H., Wesche, C., and Mata, M. M.: Three Years of Near-Coastal Antarctic Iceberg Distribution From a Machine Learning Approach Applied to SAR Imagery, J. Geophys. Res.-Oceans, 124, 6658–6672, https://doi.org/10.1029/2019JC015205, 2019b. 4. Barbat, M. M., Rackow, T., Wesche, C., Hellmer, H. H., and Mata, M. M.: Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study, ISPRS J. Photogramm., 172, 189–206, https://doi.org/10.1016/j.isprsjprs.2020.12.006, 2021. 5. Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Au- 55 tomated extraction of antarctic glacier and ice shelf fronts from Sentinel-1 imagery using deep learning, Remote Sens., 11, 1–22, https://doi.org/10.3390/rs11212529, 2019.
|
|