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Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar

Ressel, Rudolf and Singha, Suman and Lehner, Susanne and Rösel, Anja and Spreen, Gunnar (2016) Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9 (7), pp. 3131-3143. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2016.2539501 ISSN 1939-1404

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Official URL: http://dx.doi.org/10.1109/JSTARS.2016.2539501

Abstract

Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea icemonitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH–VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to bemore useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.

Item URL in elib:https://elib.dlr.de/98219/
Document Type:Article
Additional Information:Special Issue on “GeoVision: Computer Vision for Geospatial Applications”; PDF with open access
Title:Investigation into different polarimetric features for sea ice classification using X-band Synthetic Aperture Radar
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ressel, RudolfRudolf.Ressel (at) dlr.deUNSPECIFIED
Singha, SumanSuman.Singha (at) dlr.dehttps://orcid.org/0000-0002-1880-6868
Lehner, SusanneSusanne.Lehner (at) dlr.deUNSPECIFIED
Rösel, AnjaNorwegian Polar InstituteUNSPECIFIED
Spreen, GunnarUniversity Bremen IUPUNSPECIFIED
Date:2 August 2016
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:9
DOI :10.1109/JSTARS.2016.2539501
Page Range:pp. 3131-3143
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:Special Issue on “GeoVision: Computer Vision for Geospatial Applications”
ISSN:1939-1404
Status:Published
Keywords:Sea ice classification, polarimetry, TerraSAR-X, Artificial Neural Network, Feature Evaluation
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 - Vorhaben Entwicklung und Erprobung von Verfahren zur Gewässerfernerkundung (old)
Location: Bremen , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Remote Sensing Technology Institute
Deposited By: Kaps, Ruth
Deposited On:22 Jan 2016 08:59
Last Modified:31 Jul 2019 19:55

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