Delineating giant Antarctic Icebergs with Deep Learning ...
<!--!introduction!--> Icebergs account for half of all ice loss from Antarctica. Their melting affects the surrounding ocean properties through the intrusion of cold, fresh meltwater and the release of terrigenious nutrients. This in turn influences the local ocean circulation, sea ice formati...
| Main Authors: | , , , |
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-2663 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019231 |
| Summary: | <!--!introduction!--> Icebergs account for half of all ice loss from Antarctica. Their melting affects the surrounding ocean properties through the intrusion of cold, fresh meltwater and the release of terrigenious nutrients. This in turn influences the local ocean circulation, sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, we need to track them and monitor changes in their area and thickness. While the locations of large icebergs are tracked operationally by manual inspection, delineation of iceberg extent requires detailed analysis – either also manually or through automated segmentation of high resolution satellite imagery. In this study, we propose a U-net approach to automatically segment giant icebergs in nearly 200 Sentinel-1 images. It is the first study to apply a deep learning algorithm to iceberg segmentation. Furthermore, most previous studies to detect icebergs have focused on smaller bergs. In contrast, we aim to segment ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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