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Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning

Baumhoer, Celia and Dietz, Andreas and Kneisel, Christoph and Kuenzer, Claudia (2019) Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning. Remote Sensing, 11 (21), pp. 1-22. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs11212529 ISSN 2072-4292

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Abstract

Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.

Item URL in elib:https://elib.dlr.de/129964/
Document Type:Article
Title:Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Baumhoer, CeliaCelia.Baumhoer (at) dlr.deUNSPECIFIED
Dietz, AndreasAndreas.Dietz (at) dlr.deUNSPECIFIED
Kneisel, Christophkneisel (at) uni-wuerzburg.deUNSPECIFIED
Kuenzer, Claudiaclaudia.kuenzer (at) dlr.deUNSPECIFIED
Date:29 October 2019
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:11
DOI :10.3390/rs11212529
Page Range:pp. 1-22
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:Antarctica; coastline; deep learning; semantic segmentation; Getz Ice Shelf; calving front; glacier front; U-Net; convolutional neural network; glacier terminus
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Baumhoer, Celia
Deposited On:05 Nov 2019 12:09
Last Modified:14 Dec 2019 04:27

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