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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.

Full text not available from this repository.

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

Abstract

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
Authors:
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
Date:2022
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:60
DOI :10.1109/TGRS.2021.3064606
Page Range:pp. 1-14
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
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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