Articles | Volume 7, issue 6
https://doi.org/10.5194/tc-7-1693-2013
https://doi.org/10.5194/tc-7-1693-2013
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. Moen, A. P. Doulgeris, S. N. Anfinsen, A. H. H. Renner, N. Hughes, S. Gerland, and T. Eltoft

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Cited articles

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, 1648–663, 2005.
Breivik, L.-A., Eastwood, S., Karvonen, J., Dinessen, F., Fleming, A., Hamre, T., Pedersen, L. T., Saldo, R., and Buus-Hinkler, J.: Quality information document for OSI TAC sea ice products 011-001, -002, -003, -004, -006, -007, -009, -010, -011, -012, Technical report MYO2-OSI-QUID-011-ALL, version 1.3, Oslo, available at: http://catalogue.myocean.eu.org/static/resources/myocean/quid/MYO2-OSI-QUID-011-ALL-V1.3.pdf, 2012.
Charbonneau, F., Brisco, B., R.K. Raney, H. M., Liu, C., Vachon, P., Shang, J., DeAbreu, R., Champagne, C., Merzouki, A., and Geldsetzer, T.: Compact polarimetry overiew and applications assessment, Can. J. Remote Sens. Suppl., 36, S298–S315, 2010.
Clausi, D. A. and Deng, H.: Operational segmentation and classification of SAR sea ice imagery, IEEE workshop on Advances in techniques for analysis of remotely sensed data, 268–275, 2004.
Clausi, D. A., Qin, A. K., Chowdhury, M. S., Yu, P., and Maillard, P.: MAGIC: MAp-Guided Ice Classification System, Can. J. Remote Sens. Suppl., 36, S13–S25, 2010.