Extracting Glacier Calving Fronts by Deep Learning: The Benefit of Multispectral, Topographic, and Textural Input Features-Reference-Cited by-同舟云学术

Extracting Glacier Calving Fronts by Deep Learning: The Benefit of Multispectral, Topographic, and Textural Input Features

Published:2022 Issue: Volume:60 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Loebel Erik¹^ORCID,Scheinert Mirko¹^ORCID,Horwath Martin¹^ORCID,Heidler Konrad²^ORCID,Christmann Julia³^ORCID,Phan Long Duc³,Humbert Angelika³^ORCID,Zhu Xiao Xiang²^ORCID

Affiliation:

1. Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany

2. German Aerospace Center, Remote Sensing Technology Institute, Wessling, Germany

3. Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung, Sektion Glaziologie, Bremerhaven, Germany

Funder

Helmholtz Association

Helmholtz Information and Data Science Incubator Project “Artificial Intelligence for Cold Regions,”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09896900.pdf?arnumber=9896900

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3. An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery

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

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

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