A sea ice concentration estimation algorithm utilizing radiometer and SAR data

Cryosphere

Volumn 8, Issue 5, 2014, Pages 1639-1650

  Karvonen, J ^a  

^a FINNISH METEOROLOGICAL INSTITUTE   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BOOTSTRAPPING; CONCENTRATION (COMPOSITION); DIGITAL MAPPING; POLARIZATION; RADIOMETER; SEA ICE; SYNTHETIC APERTURE RADAR;

GERMANY; HAMBURG [GERMANY];

EID: 84906997291     PISSN: 19940416     EISSN: 19940424     Source Type: Journal    
DOI: 10.5194/tc-8-1639-2014     Document Type: Article

References (40)

1. Beitsch, A., Kaleschke, L., and Kern, S.: AMSR2 ASI 3.125 km Sea Ice Concentration Data, V0.1, Institute of Oceanography, University of Hamburg, Germany, digital media, available at: ftp://ftp-projects.zmaw.de/seaice/ (last access: 31 July 2014), 2013.
2. Beitsch, A., Kaleschke, L., and Kern, S.: The February 2013 Arctic Sea Ice Fracture in the Beaufort Sea - a case study for two different AMSR2 sea ice concentration algorithms, Remote Sensing, 6, 3841-3856, 2014.
3. Berg, A.: Spaceborne SAR in Sea Ice Monitoring: Algorithm Development and Validation for the Baltic Sea, Licentiate thesis, Technical Report 47L, Chalmers University of Technology, Gothenburg, Sweden, 2011.
4. Berthod, M., Kato, Z., Yu, S., and Zerubia, J.: Bayesian image classification using Markov Random Fields, Image Vision Comput., 14, 285-295, 1996.
5. Besag, J.: On the statistical analysis of dirty pictures, J. Roy. Stat. Soc. B, 48, 259-302, 1986.
6. Bovith, T. and Andersen, S.: Sea Ice Concentration from Single-Polarized SAR data using Second-Order Grey Level Statistics and Learning Vector Quantization, Scientific Report 05-04, Danish Meteorological Institute, Copenhagen, Denmark, 2005.
7. Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res., 89, 5355-5369, 1984.
8. Cavalieri, D. J., Markus, T., Hall, D. K., Gasiewski, A. J., Klein, M., and Ivanoff, A.: Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentrations Using Landsat-7 and Airborne Microwave Imagery, IEEE T. Geosci. Remote, 44, 3057-3069, 2006.
9. Clausi, D. A.: Comparison and fusion of co-occurrence, Gabor, and MRF texture features for classification of SAR sea ice imagery, Atmos. Oceans, 39, 183-194, 2001.
10. Clausi, D. A. and Jernigan, M. E.: Designing Gabor filters for optimal texture separability, Pattern Recogn., 33, 1835-1849, 2000.
11. Comiso, J. C.: Characteristics of Arctic winter sea ice from satellite multispectral microwave observations, J. Geophys. Res., 91, 975-994, 1986.
12. Comiso, J. C.: SSM/I Sea Ice Concentrations Using the Bootstrap Algorithm, NASA Reference Publication/Goddard Space Flight Center 1380, Greenbelt, Maryland, USA, 1995.
13. Deng, H. and Clausi, D. A.: Gaussian MRF rotation-invariant features for image classification, IEEE T. Pattern Anal., 26, 951-955, 2004.
14. Deng, H. and Clausi, D. A.: Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model, IEEE T. Geosci. Remote, 43, 528-538, 2005.
15. Dokken, S. T., Hakansson, B., and Askne, J.: Inter-comparison of Arctic Sea ice concentration using RADARSAT, ERS, SSM/I and in-situ data, Can. J. Remote Sens., 26, 521-536, 2000.
16. Drüe, C. and Heinemann, G.: High-resolution maps of the sea-ice concentration from MODIS satellite data, Geophys. Res. Lett., 31, L20403, doi: 10.1029/2004GL020808, 2004.
17. Haralick, R. M., Shanmugam, K., and Dinstein, I.: Textural features for image classification, IEEE T. Syst. Man Cyb., SMC-3, 610-621, 1973.
18. Haykin, S. S.: Neural Networks, a Comprehensive Foundation, 2nd Edn., Prentice Hall, Upper Saddle River, N.J., USA, 183-196, 1999.
19. Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-366, 1989.
20. JAXA: Descriptions of GCOM-W1 AMSR2 Level 1R and Level 2 Algorithms, Rev. A, available at: http://suzaku.eorc.jaxa.jp/GCOM-W/data/data-w-algorithm.html (last access: 31 July 2014), 2013.
21. Kaleschke, L. and Kern, S.: ERS-2 SAR Image Analysis for Sea Ice Classification in the Marginal Ice Zone, Proc. IEEE International geoscience and remote sensing symposium 2000 (IGARSS 2000), V, 3038-3040, 24-28 July, 2000, Honolulu, Hawaii, USA, 2000.
22. Kaleschke, L., Lupkes, C., Vihma, T., Haarpaintner, J., Bochert, A., Hartmann, J., and Heygster, G.: SSM/I sea ice remote sensing for mesoscale ocean-atmosphere interaction analysis, Can. J. Remote Sens., 27, 526-537, 2001.
23. Karvonen, J.: Baltic Sea ice concentration estimation based on CBand HH-Polarized SAR data, IEEE J. Sel. Top. Appl., 5, 1874-1884, doi: 10.1109/JSTARS.2012.2209199, 2012.
24. Karvonen, J.: Baltic Sea ice concentration estimation based on CB and Dual-Polarized SAR data, IEEE T. Geosci. Remote, 52, 5558-5566, doi: 10.1109/TGRS.2013.2290331, 2014.
25. Karvonen, J., Cheng, B., and Simila, M.: Ice Thickness Charts Produced by C-Band SAR Imagery and HIGHTSI Thermodynamic Ice Model, Proc. of the Sixth Workshop on Baltic Sea Ice Climate, 71-81, 25-28 August 2008, Lammi, Finland 2008.
26. Karvonen, J., Cheng, B., Vihma, T., Arkett, M., and Carrieres, T.: A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model, The Cryosphere, 6, 1507-1526, doi: 10.5194/tc-6-1507-2012, 2012.
27. Kato, Z., Zerubia, J., and Berthod, M.: Satellite Image Classification Using a Modified Metropolis Dynamics, in: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 92), 3, 23-26 March 1992, San Francisco, California, USA, 573-576, March, 1992.
28. Leigh, S., Wang, Z., and Clausi, D. A.: Automated ice-water classification using dual polarization SAR satellite imagery, IEEE T. Geosci. Remote, 52, 5529-5539, 2014.
29. Maass, N. and Kaleschke, L.: Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea, Tellus, 62A, 393-410, 2010.
30. MacQueen, J. B.: Some Methods for classification and Analysis of Multivariate Observations, in: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 21 June-18 July 1965 and 27 December 1965-7 June 1966, University of California Press., 281-297, 1967.
31. Maeda, T.: AMSR2 L1R Product, JAXA, available at: http://suzaku. eorc.jaxa.jp/GCOM-W/materials/product/AMSR2-L1R.pdf (last access: 31 July 2014), 2013.
32. Maillard, P., Clausi, D. A., and Deng, H.: Map-guided sea ice segmentation and classification using SAR imagery and a MRF segmentation scheme, IEEE T. Geosci. Remote, 43, 2940-2951, 2005.
33. Makynen, M., Manninen, T., Simila, M., Karvonen, J., and Hallikainen, M.: Incidence angle dependence of the statistical properties of the C-Band HH-Polarization backscattering signatures of the Baltic Sea Ice, IEEE T. Geosci. Remote, 40, 2593-2605, 2002.
34. Ochilov, S. and Clausi, D. A.: Operational SAR sea-ice classification, IEEE T. Geosci. Remote, 50, 4397-4408, 2012.
35. Pichler, O., Teuner, A. S., and Hosticka, B. J.: A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms, Pattern Recogn., 29, 733-742, 1996.
36. Rue, H. and Held, L.: Gaussian Markov Random Fields: Theory and Applications, CRC Press, 2005.
37. Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res., 113, C02S03, doi: 10.1029/2005JC003384, 2008.
38. Steffen, K., Key, J., Cavalieri, D. J., Comiso, J., Gloersen, P., St. Germain, K., and Rubinstein, I.: The estimation of geophysical parameters using passive microwave algorithms, Ch. 10, in: Microwave Remote Sensing of Sea Ice, edited by: Carsey, F. D., American Geophysical Society/Wiley, Washington D.C., USA, 201-231, 1992.
39. Yu, Q. and Clausi, D. A.: SAR sea-ice image analysis based on iterative region growing using semantics, IEEE T. Geosci. Remote, 45, 3919-3931, 2007.
40. Wessel, P. and Smith, W. H. F.: A global self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101, 8741-8743, 1996.