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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 1
The Cryosphere, 11, 33–46, 2017
https://doi.org/10.5194/tc-11-33-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 11, 33–46, 2017
https://doi.org/10.5194/tc-11-33-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 11 Jan 2017

Research article | 11 Jan 2017

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Natalia Zakhvatkina et al.

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Cited articles

Albregtsen, F.: Statistical Texture Measures Computed from Gray Level Coocurrence Matrices, Image Processing Laboratory Department of Informatics University of Oslo, 5 November 2008.
Bogdanov, A. V., Sandven, S., Johannessen, O. M., Alexandrov, V. Y., and Bobylev, L. P.: Multisensor approach to automated classification of sea ice image data, IEEE T. Geosci. Remote, 43, 1648–1664, 2005.
Clausi, D. A.: An analysis of co-occurrence texture statistics as a function of grey level quantization, Can. J. Remote Sens., 28, 45–62, 2002.
Clausi, D. A., Qin, A. K., Chowdhury, M. S., Yu, P., and Maillard, P.: MAGIC: MAp-Guided Ice Classification System, Can. J. Remote Sens., 36, suppl. 1, S13–S25, 2010.
Cortes, C. and Vapnik, V.: Support-Vector Networks, Mach. Learn., 20, 273–297, 1995.
Publications Copernicus
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The presented fully automated algorithm distinguishes open water (rough/calm) and sea ice based on dual-polarized RS2 SAR images. Texture features are used for Support Vector Machines supervised image classification. The algorithm includes pre-processing and validation procedures. More than 2700 scenes were processed and the results show the good discrimination between open water and sea ice areas with accuracy 91 % compared with ice charts produced by MET Norway service.
The presented fully automated algorithm distinguishes open water (rough/calm) and sea ice based...
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