Ressel, Rudolf und Frost, Anja und 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), Seiten 3672-3680. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2015.2436993. ISSN 1939-1404.
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Offizielle URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7122229
Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/90934/ | ||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||
Titel: | A Neural Network Based Classification for Sea Ice Types on X-Band SAR Images | ||||||||||||||||
Autoren: |
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Datum: | 2015 | ||||||||||||||||
Erschienen in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Nein | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||
Band: | 8 | ||||||||||||||||
DOI: | 10.1109/JSTARS.2015.2436993 | ||||||||||||||||
Seitenbereich: | Seiten 3672-3680 | ||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||
Name der Reihe: | SPECIAL ISSUE ON JOINT IGARSS 2014/35th CANADIAN SYMPOSIUM ON REMOTE SENSING | ||||||||||||||||
ISSN: | 1939-1404 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | texture, pattern analysis, remote sensing, earth and atmospheric sciences | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Entwicklung und Erprobung von Verfahren zur Gewässerfernerkundung (alt) | ||||||||||||||||
Standort: | Bremen , Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > SAR-Signalverarbeitung Institut für Methodik der Fernerkundung | ||||||||||||||||
Hinterlegt von: | Kaps, Ruth | ||||||||||||||||
Hinterlegt am: | 26 Mai 2015 09:45 | ||||||||||||||||
Letzte Änderung: | 28 Nov 2023 09:04 |
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