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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 6
The Cryosphere, 7, 1693–1705, 2013
https://doi.org/10.5194/tc-7-1693-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Earth observation of the Cryosphere

The Cryosphere, 7, 1693–1705, 2013
https://doi.org/10.5194/tc-7-1693-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Nov 2013

Research article | 07 Nov 2013

Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts

M. -A. N. Moen1, A. P. Doulgeris1, S. N. Anfinsen1, A. H. H. Renner2, N. Hughes3, S. Gerland2, and T. Eltoft1,4 M. -A. N. Moen et al.
  • 1Department of Physics and Technology, University of Tromsø, 9037 Tromsø, Norway
  • 2Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
  • 3Norwegian Ice Service, Norwegian Meteorological Institute, 9293 Tromsø, Norway
  • 4Northern Research Institute, 9294 Tromsø, Norway

Abstract. In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Utilizing polarimetric information in sea ice charting will increase the efficiency and exactness of the maps. The number of classes used in the segmentation has shown to be of significant importance. Thus, studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.

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