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Sea-ice type classification from ERS-1 SAR data based on grey level and texture information

Published online by Cambridge University Press:  27 October 2009

D.M. Smith
Affiliation:
Remote Sensing Unit, Department of Geography, University of Bristol, Bristol BS8 1SS
E.C. Barrett
Affiliation:
Remote Sensing Unit, Department of Geography, University of Bristol, Bristol BS8 1SS
J.C. Scott
Affiliation:
Defence Research Agency, Southwell, Portland, Dorset DT5 2JS

Abstract

This paper describes the development of a practical algorithm for the classification of sea-ice types from ERS-1 synthetic aperture radar (SAR) data. The algorithm was based on a combination of grey level and texture information in order to overcome ambiguous grey level values of different ice types. The problem of calculating texture parameters for windows containing more than one ice type was overcome by first segmenting the image so that only pixels from the same segment were included in the calculation of the texture measure. The segmentation procedure was based on the iterative application of a speckle noise reduction filter, and was thus crucially dependent on the ability of such a filter to smooth out noise without destroying edges and fine features. In order to achieve this, a modification to the sigma filter of Lee (1983b) was developed; it out-performed the sigma filter for a model problem. Two ERS-1 SAR scenes of the marginal ice zone east of Spitsbergen in March 1992 were analysed by calculating values of grey level and range for different ice types contained within raw data extracts. Although the grey levels of some of the ice types overlapped, most of the ambiguity was removed through the additional use of range. It was also necessary to test for the wave-like appearance of open water. The classification scheme was demonstrated to identify correctly most of the grease/new ice, first-year ice, multiyear ice, rough ice, pancake ice, and open water in the two SAR scenes, although there was some misclassification of open water as first-year ice.

Type
Articles
Copyright
Copyright © Cambridge University Press 1995

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References

Barber, D.J., and LeDrew, E.F.. 1991. SAR sea ice discrimination using texture statistics: a mutivariate approach. Photogrammetric Engineering and Remote Sensing 57: 385395.Google Scholar
Barry, R.G., Serreze, M.C., Maslanik, J.A., and Preller, R.H.. 1993. The Arctic sea ice–climate system: observations and modeling. Reviews of Geophysics 31: 397422.CrossRefGoogle Scholar
Cavalieri, D.J. 1994. A microwave technique for mapping thin ice. Journal of Geophysical Research 99: 12,561–12,572.CrossRefGoogle Scholar
Caves, R.G., Quegan, S., and White, R.G.. 1993. The use of segmentation for change detection in spaceborne SAR Images. In: Hilton, K. (editor). Towards operational applications: proceedings of the 19th annual conference of the Remote Sensing Society. Nottingham: Remote Sensing Society: 168175.Google Scholar
Durand, J.M., Gimonet, B.J., and Perbos, J.R.. 1987. SAR data filtering for classification. IEEE Transactions on Geoscience and Remote Sensing 25: 629637.CrossRefGoogle Scholar
Frost, V.S., Stiles, J.A., Shanmugan, K.S., and Holtzman, J.C.. 1982. A model for radar images and its application to digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence 4: 157165.CrossRefGoogle Scholar
Fu, K.S., and Mui, J.K.. 1981. A survey on image segmentation. Pattern Recognition 13: 316.CrossRefGoogle Scholar
Haralick, R.M., Shanmugan, K., and Dinstein, I.. 1973. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 6: 610621.Google Scholar
Holmes, Q.A., Nüesch, D.R., and Shuchman, R.A.. 1984. Textural analysis and real-time classification of sea-ice types using digital SAR data. IEEE Transactions on Geoscience and Remote Sensing 22: 113120.CrossRefGoogle Scholar
Kwok, R., Rignot, E., Holt, B., and Onstott, R.. 1992. Identification of sea ice types in spaceborne synthetic aperture radar. Journal of Geophysical Research 97: 23912402.CrossRefGoogle Scholar
Laur, H. 1992. Derivation of backscattering coefficient σ° in ERS-1.SAR.PRI products. Noordwijk: European Space Agency.Google Scholar
Lee, J.S. 1981. Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing 17: 2432.CrossRefGoogle Scholar
Lee, J.S. 1983a. Digital image smoothing and the sigma filter. Computer Vision, Graphics and Image Processing 24: 255269.Google Scholar
Lee, J.S. 1983b. A simple speckle smoothing algorithm for synthetic aperture radar images. IEEE Transactions on Systems, Man and Cybernetics 13: 8589.CrossRefGoogle Scholar
Lee, J.S. 1986. Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering 25: 636643.Google Scholar
Lee, J.S., and Jurkevich, I.. 1989. Segmentation of SAR images. IEEE Transactions on Geoscience and Remote Sensing 27: 674680.Google Scholar
Livingstone, C.E., Onstott, R.G., Arsenault, L.D., Gray, A.L, and Singh, K.P.. 1987. Microwave ice signatures near the onset of melt. IEEE Transactions on Geoscience and Remote Sensing 25: 174187.Google Scholar
Massom, R., and Comiso, J.C.. 1991. The detection of new sea ice and surface temperature using Advanced Very High Resolution Radiometer and Special Sensor Microwave/lmager satellite data. In: Putkonen, J. (editor). Remote sensing: global monitoring for Earth management: proceedings of IGARSS '91 symposium, Espoo, Finland, June 3–6, 1991. Piscataway, NJ: Institute of Electrical and Electronics Engineers: II, 791795.Google Scholar
Meadows, P.J. 1994. The calibration of the ERS-1 SAR using UK PAF imaging. In: Proceedings of EARSeL Symposium, Göteberg, 6–8 June 1994. Noordwijk: European Space Agency Publications Division.Google Scholar
Nystuen, J.A., and Garcia, F.W.. 1992. Sea ice classification using SAR backscatter statistics. IEEE Transactions on Geoscience and Remote Sensing 30: 502509.CrossRefGoogle Scholar
Onstott, R.G., Grenfell, T.C., Mätzler, C., Luther, C.L., and Svendsen, E.A.. 1987. Evolution of microwave sea ice signatures during early summer and midsummer in the marginal ice zone. Journal of Geophysical Research 92: 68256835.CrossRefGoogle Scholar
Rauste, Y. 1990. Use of texture features in discrimination of sea ice types in SAR images. In: Remote sensing for the nineties: proceedings of IGARSS '90 symposium. Piscataway, NJ: Institute of Electrical and Electronics Engineers: III, 22292232.Google Scholar
Rees, W.G. 1990. Physical principles of remote sensing. Cambridge: Cambridge University Press.Google Scholar
Sahoo, P.K., Soltani, S., Wong, A.K.C., and Chen, Y.C.. 1988. A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41: 233260.CrossRefGoogle Scholar
Sandven, S., and Johannessen, O.M.. 1993. Ice studies in the Barents Sea by ERS-1 SAR during SIZEX '92. Advances in Space Research 13 (5): 4152.CrossRefGoogle Scholar
Sandven, S., Kloster, K., Johannessen, O.M., and Miles, M.. 1993. SIZEX '92 ERS-1 SAR Ice Validation Experiment: final report to European Space Agency and Norwegian Space Center. Bergen: Nansen Environmental and Remote Sensing Center (Technical Report 69).Google Scholar
Sephton, A.J., Brown, L.M.J., Macklin, J.T., Partington, K.C., Veck, N.J., and Rees, W.G.. 1994. Segmentation of synthetic-aperture radar imagery of sea ice. International Journal of Remote Sensing 15: 803825.Google Scholar
Shokr, M.E. 1990. On sea ice characterization from SAR images. IEEE Transactions on Geoscience and Remote Sensing 28: 737740.Google Scholar
Shokr, M.E. 1991. Evaluation of second-order texture parameters for sea ice classification from radar images. Journal of Geophysical Research 96: 10,625–10,640.Google Scholar
Skriver, H., and Gudmandsen, P.. 1985. Sea ice parameter retrieval from SAR data. In: Guyenne, T.D. (editor). Proceedings of the workshop on thematic applications of SAR data, Frascati, Italy, 9–11 September 1985. Noordwijk: European Space Agency Publications Division (ESA SP-257): 2128.Google Scholar
Smith, D.M., and Barrett, E.C.. 1994. Satellite mapping and monitoring of sea ice. Unpublished report to the Defence Research Agency (Maritime Division), Contract Ref. CB/RAE/9/4/2034/113/ARE, Remote Sensing Unit, University of Bristol.Google Scholar
Sun, Y., Carlström, A., and Askne, J.. 1992. SAR image classification of ice in the Gulf of Bothnia. International Journal of Remote Sensing 13: 24892514.Google Scholar