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/ | ||||||||||||
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Document Type: | Article | ||||||||||||
Title: | A Neural Network Based Classification for Sea Ice Types on X-Band SAR Images | ||||||||||||
Authors: |
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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 - Earth Observation | ||||||||||||
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: | 19 Nov 2021 20:28 |
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