Browse > Article
http://dx.doi.org/10.7780/kjrs.2013.29.1.1

Classification for Landfast Ice Types in the Greenland of the Arctic by Using Multifrequency SAR Images  

Hwang, Do-Hyun (Department of Spatial Information Engineering, Pukyong National University)
Hwang, Byongjun (The Scottish Association for Marine Science)
Yoon, Hong-Joo (Department of Spatial Information Engineering, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.29, no.1, 2013 , pp. 1-9 More about this Journal
Abstract
To classify the landfast ice in the north of the Greenland, observation data, multifrequency Synthetic Aperture Radar (SAR) images and texture images were used. The total four types of sea ice are first year ice, highly deformed ice, ridge and moderately deformed ice. The texture images that were processed by K-means algorithm showed higher accuracy than the ones that were processed by SAR images; however, overall accuracy of maximum likelihood algorithm using texture images did not show the highest accuracy all the time. It turned out that when using K-means algorithm, the accuracy of the multi SAR images were higher than the single SAR image. When using the maximum likelihood algorithm, the results of single and multi SAR images are differ from each other, therefore, maximum likelihood algorithm method should be used properly.
Keywords
Arctic; synthetic aperture radar (SAR); K-means; maximum likelihood;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Gibson, P.J., C.H. Power, and J. Keating, 2000. Introductory Remote Sensing Principles and Concepts, Routledge, NewYork.
2 Haralick, R.M., K. Shanmugam, and I.H. Dinstein, 1973. Textual features for image classification, IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6): 610-621.   DOI
3 IPCC, 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland.
4 Jackson, C.R. and J.R. Apel, 2004. Synthetic Aperture Radar marine user's manual, US Department of Commerce.
5 Kwok, R., E. Rignot, B. Holt, and R. Onstott, 1992. Identification of sea ice types in spaceborne Synthetic Aperture Radar data, Journal of Geophysical Research, 97(C2): 2391-2402.   DOI
6 Lee, K., S.-H. Jeon, and B.-D. Kwon, 2005. Implementation of GLCM/GLDV-based texture algorithm and its application to high resolution imagery analysis, Korean Journal of Remote Sensing, 21(2): 121-133(in Korean with English abstract).   과학기술학회마을   DOI
7 Mo, M.-J. and W.-H. Kim, 2000. Multiple texture image analysis and classification using spatial property, Proc. of the Korea Institute of Signal Processing and Systems Conference, December, 1(2): 105-108.   과학기술학회마을
8 Nystuen, J.A. and F.W. Garcia Jr., 1992. Sea ice classification using SAR backscatter statistics, IEEE Transactions on Geoscience and Remote Sensing, 30(3): 502-509.   DOI   ScienceOn
9 Richards, J.A. and X. Jia, 1999. Remote Sensing Digital Image Analysis an Introduction, Springer, London, NewYork.
10 Sun, Y., A. Carlstrom, and J. Askne, 1992. SAR image classification of ice in the Gulf of Bothnia, International Journal of Remote Sensing, 13(13): 2489-2514.   DOI   ScienceOn
11 Tou, J.T. and R.C. Gonzalez, 1974. Pattern Recognition Principles, Addison-Wesley Publishing Company, Massachusetts.
12 World Meteorological Organization, 1970. WMO Sea-ice nomenclature, 1970ed. Secretariat of the World Meteorological Organization, Geneva.
13 Bogdanov, A.V., S. Sandven, O.M. Johannessen, V.Y. Alexandrov, and L.P. Bobylev, 2005. Multisensor approach to automated classification of sea ice image data, IEEE Transactions on Geoscience and Remote Sensing, 43(7): 1648-1664.   DOI   ScienceOn
14 Comiso, J.C., C.L. Parkinson, R. Gersten, and L. Stock, 2008. Accelerated decline in the Arctic sea ice cover, Geophysical research Letters, 35(1): L01703.   DOI   ScienceOn