Mapping of large magnitude discontinuous sea ice motion

ABSTRACT

In this work, we attempt to extend the body of knowledge on sea ice motion tracking in two specific directions. The first direction is the development of a computationally efficient, high resolution motion tracking system with a resolution of 400m, which is an order of magnitude greater than the currently available standard data products (3--5km). Validation of this method using GPS measurements shows an average error that is less than 0.06cm/s. The second direction is the development of objective analysis technique to handle motion at close proximity to discontinuities. The goal of this second direction is to identify and track discontinuous features such as cracks, leads, ridges and other material damage zones. These developments allow motion to be estimated at a high resolution in a robust manner (validated against various noise models). With the observed changes in global climate, sparked by variations in the sea ice thickness and extent, our long term goal is to use this system to merge the "temporally rich" GPS measurements with the "spatially rich" measurements from satellite images.

REFERENCES

• 1: F. L. Bookstein, Principal Warps: Thin-Plate Splines and the Decomposition of Deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.11 n.6, p.567-585, June 1989 [doi>10.1109/34.24792]
• 2: N. R. C. Toward an Integrated Arctic Observing Network. National Academies Press, 2006.
• 3: Brian Cabral , Leith Casey Leedom, Imaging vector fields using line integral convolution, Proceedings of the 20th annual conference on Computer graphics and interactive techniques, p.263-270, August 02-06, 1993, Anaheim, CA [doi>10.1145/166117.166151]
• 4: A. Criminisi, P. Perez, and K. Toyama. Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Processing, 13(9):1200--1212, September 2004.
• 5: C. A. Geiger, S. F. Ackley, and I. W. D. Hibler. Antarctic Sea Ice: Physical Processes, Interactions and Variability, volume 74, chapter Sea ice drift and deformation processes in the western Weddell Sea, pages 141--160. Antarctic Research Series, 1998.
• 6: C. A. Geiger and M. Drinkwater. Coincident buoy-and SAR-derived surface fluxes in the western weddell sea during ice station weddell 1992. Journal of Geophysical Research, 110(C04002), 2005.
• 7: O. M. Johannessen, M. Miles, and E. Bjorgo. The Arctic's shrinking sea ice. Nature, 376:126--127, 2002.
• 8: R. Kwok, J. C. Curlander, R. McConnell, and S. Pang. An ice motion tracking system at the alaska SAR facility. IEEE J. Oceanic Eng., 15(1):44--54, 1990.
• 9: J. Lewis. Fast normalized cross-correlation. In Vision Interface, pages 120--123. Canadian Image Processing and Pattern Recognition Society, 1995.
• 10: R. W. Lindsay, J. Zhang, and D. A. Rothrock. Sea ice deformation rates from measurements and in a model. Atmosphere-Oceans, 40:35--47, 2003.
• 11: J. A. Maslanik, M. C. Serreze, and R. G. Barry. Recent decreases in arctic summer ice cover and linkages to atmospheric circulation anomalies. Geophys. Res. Lett., 23(13):1677--1680, 1996.
• 12: H. L. Stern and R. E. Moritz. Sea ice kinematics and surface properties from radarsat synthetic aperture radar during the sheba drift. J. Geophys. Res., 107(C10), 2002.
• 13: M. Thomas. Analysis of Large Magnitude Discontinuous Non-rigid Motion. PhD thesis, University of Delaware, December 2008.
• 14: M. Thomas, C. A. Geiger, and C. Kambhamettu. High resolution motion estimation of sea ice using an implicit quad-tree approach. In ISPRS Workshop on High-Resolution Earth Imaging for Geospatial Information, Hannover, May 2007.
• 15: M. Thomas , C. A. Geiger , C. Kambhamettu, Vector field characterization in ERS-1 imagery of sea ice, Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision, p.23, February 21-22, 2007 [doi>10.1109/WACV.2007.62]
• 16: M. Thomas, C. A. Geiger, and C. Kambhamettu. High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. Journal of Cold Regions Science and Technology, 52(2):207--223, 2008.
• 17: M. Thomas, C. A. Geiger, C. Kambhamettu, and P. Kannan. Streamline regularization for large discontinuous motion of sea ice. In Workshop on Patt. Rec. in Remote Sensing, December 2008.
• 18: M. Thomas , C. Kambhamettu , C. A. Geiger , J. Hutchings , M. Engram, Near-real time motion analysis for APLIS 2007: a systems modeling perspective, Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, November 07-09, 2007, Seattle, Washington [doi>10.1145/1341012.1341032]
• 19: David Vernon, Fourier Vision: Segmentation and Velocity Measurement Using the Fourier Transform, Kluwer Academic Publishers, Norwell, MA, 2001

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.2 Approximation

  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING

General Terms:
Algorithms, Design, Measurement, Performance

Keywords:
Fourier theory, cryosphere, discontinuity identification, line integral convolution, mapping, near real time motion analysis