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Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements

Singha, Suman and Johansson, A. Malin and Hughes, Nicholas and Hvidegaard, Sine M. and Skourup, Henriette (2018) Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements. IEEE Transactions on Geoscience and Remote Sensing, 56 (7), pp. 3715-3734. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2018.2809504. ISSN 0196-2892.

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Official URL: https://doi.org/10.1109/TGRS.2018.2809504

Abstract

In recent years, spaceborne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice analysis. Here, we employ an automatic sea ice classification algorithm on two sets of spatially and temporally near coincident fully polarimetric acquisitions from the ALOS-2, Radarsat-2, and TerraSAR-X/TanDEM-X satellites. Overlapping coincident sea ice freeboard measurements from airborne laser scanner data are used to validate the classification results. The automated sea ice classification algorithm consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. Next, the resulting feature vectors are ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. Coherency matrix-based features that require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix-based features, which makes coherency matrix-based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant-based features (geometric intensity, scattering diversity, and surface scattering fraction). Classification results show that 100% of the open water is separated from the surrounding sea ice and that the sea ice classes have at least 96.9% accuracy. This analysis reveals analogous results for both X-band and C-band frequencies and slightly different for the L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected sea ice when compared with high-resolution airborne measurements.

Item URL in elib:https://elib.dlr.de/113943/
Document Type:Article
Additional Information:corresponding author: Suman.Singha@dlr.de
Title:Arctic Sea Ice Characterization using Spaceborne Fully Polarimetric L-, C- and X-Band SAR with Validation by Airborne Measurements
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Singha, SumanSuman.Singha (at) dlr.dehttps://orcid.org/0000-0002-1880-6868
Johansson, A. MalinUiT The Arctic University of Norway, Department of Physics and Technology, Tromsø, NorwayUNSPECIFIED
Hughes, NicholasNorwegian Ice Service, Norwegian Meteorological Institute, 9293 Tromsø, NorwayUNSPECIFIED
Hvidegaard, Sine M.National Space Institute, Technical University of Denmark, 2800 Kgs. Lyngby, DenmarkUNSPECIFIED
Skourup, HenrietteNational Space Institute, Technical University of Denmark, 2800 Kgs. Lyngby, DenmarkUNSPECIFIED
Date:26 April 2018
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:56
DOI :10.1109/TGRS.2018.2809504
Page Range:pp. 3715-3734
Editors:
EditorsEmailEditor's ORCID iD
UNSPECIFIEDAmerican Geophysical Union - AGUUNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Sea ice, Multi-Frequency SAR, Polarimetry, NRT Processing, Artificial Neural Network, Airborne Laser Scanner
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 - SAR methods
Location: Bremen , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Kaps, Ruth
Deposited On:07 Sep 2017 13:06
Last Modified:17 Jul 2019 03:00

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