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HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Heidler, Konrad and Mou, LiChao and Baumhoer, Celia and Dietz, Andreas and Zhu, Xiao Xiang (2022) HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline. IEEE Transactions on Geoscience and Remote Sensing, 60 (430051), pp. 1-14. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2021.3064606. ISSN 0196-2892.

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Official URL: https://ieeexplore.ieee.org/document/9383809


Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we, therefore, devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a data set of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at https://github.com/khdlr/HED-UNet.

Item URL in elib:https://elib.dlr.de/136951/
Document Type:Article
Title:HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Heidler, Konradkonrad.heidler (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Baumhoer, CeliaCelia.Baumhoer (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2021.3064606
Page Range:pp. 1-14
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Deep Learning, U-Net, edge detection, coastline, Antarctica, HED-Unet
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Remote Sensing Technology Institute > EO Data Science
Deposited By: Baumhoer, Celia
Deposited On:25 May 2021 09:18
Last Modified:10 Jan 2022 10:49

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