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Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results

  • Thomas Hollands (a1) and Wolfgang Dierking (a1)

Abstract

Sea-ice drift fields were obtained from sequences of synthetic aperture radar (SAR) images using a method based on pattern recognition. the accuracy of the method was estimated for two image products of the Envisat Advanced SAR (ASAR) with 25 m and 150 m pixel size. For data from the winter season it was found that 99% of the south–north and west–east components of the determined displacement vector are within ±3–5 pixels of a manually derived reference dataset, independent of the image resolution. For an image pair with 25 m resolution acquired during summer, the corresponding value is 12 pixels. Using the same resolution cell dimensions for the displacement fields in both image types, the estimated displacement components differed by 150–300 m. the use of different texture parameters for predicting the performance of the algorithm dependent on ice conditions and image characteristics was studied. It was found that high entropy values indicate a good performance.

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References

Hide All
Akima, H. 1978. A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points. ACM Trans. Math. Softw., 4(2), 148159.
Banfield, J. 1991. Automated tracking of ice floes: a stochastic approach. IEEE Trans. Geosci. Remote Sens., 29(6), 905911.
Berrisford, P. and 6 others. 2009. The ERA-Interim archive. Reading, European Centre for Medium-Range Weather Forecasts. (ERA Report Series 1.)
Canty, M.J. 2007. Image analysis, classification and change detection in remote sensing: with algorithms for ENVI/IDL. Boca Raton, FL, Taylor and Francis.
Chalermwat, P. 1999. High performance automatic image registration for remote sensing. (PhD thesis, George Mason University.)
Fily, M. and Rothrock, D.A.. 1987. Sea ice tracking by nested correlations. IEEE Trans. Geosci. Remote Sens., 25(5), 570580.
Gonzalez, R.C. and Woods, R.E.. 2008. Digital image processing. Third edition. Upper Saddle River, NJ, Pearson Education.
Gutierrez, S. and Long, D.G.. 2003. Optical flow and scale-space methods and applications in sea ice motion in Antarctica. In Stein, T., ed. IGARSS 2003, International Geoscience and Remote Sensing Symposium, 21–25 July 1999, Toulouse, France. Proceedings, Vol. 4. Piscataway, NJ, Institute of Electrical and Electronics Engineers, 28052807.
Hall, R.T. and Rothrock, D.A.. 1981. Sea ice displacement from Seasat synthetic aperture radar. J. Geophys. Res., 86(C11), 11,07811,082.
Holt, B., Rothrock, D.A. and Kwok, R.. 1992. Determination of sea ice motion from satellite images. In Carsey, F.D. and 7 others, eds. Microwave remote sensing of sea ice. Washington, DC, American Geophysical Union, 343354.
Kwok, R., Curlander, J.C., R.McConnell and Pang, S.S.. 1990. An ice-motion tracking system at the Alaska SAR facility. IEEE J. Ocean. Eng., 15(1), 4454.
McConnell, R., Kwok, R., Curlander, J.C., Kober, W. and Pang, S.. 1991. Psi-S correlation and dynamic time warping: two methods for tracking ice floes in SAR images. IEEE Trans. Geosci. Remote Sens., 29(6), 10041012.
Shannon, C.E. 1948. A mathematical theory of communication. Bell Syst. Tech. J., 27(3), 379423.
Thomas, M.V. 2008. Analysis of large magnitude discontinous non-ridge motion. (PhD thesis, University of Delaware.)
Thomas, M.V., Geiger, C.A. and Kambhamettu, C.. 2008. High resolution (400m) motion characterization of sea ice using ERS-1 SAR imagery. Cold Reg. Sci. Technol., 52(2), 207223.

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