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A Neural Network Based Classification for Sea Ice Types on X-Band SAR Images

Ressel, Rudolf and Frost, Anja and Lehner, Susanne (2015) A Neural Network Based Classification for Sea Ice Types on X-Band SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (7), pp. 3672-3680. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2015.2436993 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7122229

Abstract

We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, GLCM-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice type regime, given the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step towards operational, near real time ice charting.

Item URL in elib:https://elib.dlr.de/90934/
Document Type:Article
Title:A Neural Network Based Classification for Sea Ice Types on X-Band SAR Images
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ressel, RudolfRudolf.Ressel (at) dlr.deUNSPECIFIED
Frost, AnjaAnja.Frost (at) dlr.deUNSPECIFIED
Lehner, SusanneSusanne.Lehner (at) dlr.deUNSPECIFIED
Date:2015
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:8
DOI :10.1109/JSTARS.2015.2436993
Page Range:pp. 3672-3680
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:SPECIAL ISSUE ON JOINT IGARSS 2014/35th CANADIAN SYMPOSIUM ON REMOTE SENSING
ISSN:1939-1404
Status:Published
Keywords:texture, pattern analysis, remote sensing, earth and atmospheric sciences
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:26 May 2015 09:45
Last Modified:08 Mar 2018 18:31

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