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higher resolution using bi-cubic interpolation prior to the actual
image correlation performs better than both interpolation of the
correlation surface using the same algorithm and peak
localisation using curve fitting. Correlation peak localisation
using Gaussian and polynomial algorithms are inferior in such
applications.
Therefore, we conclude that more precise and accurate
displacement measurements are obtained by interpolating the
available images to a higher resolution using bi-cubic
interpolation prior to matching. In such approaches, one can
gain over 40% reduction in mean error by interpolating the
images to up to 1/16 th of a pixel. Interpolating to a more detailed
sub-pixel resolution than 1/16 th of a pixel does not add much.
Or in other words, when matching low-resolution images using
normalized cross-correlation with intensity-interpolation based
sub-pixel precision, 40% or better accuracy increment can be
achieved compared to pixel-precision matching of images in
reference to the same original resolution as the interpolated one.
When real low-resolution images are used together with varying
sizes of the matching entities, as opposed to the approach used
in this study, even better precision and accuracy might be
obtained as the noise due to resampling will not be present, and
template and search window sizes will be adjusted with the
pixel size.
It should also be noted that although the relative performances
of the algorithms is expected to be valid at least for other spatial
domain matching approaches and for other applications, the
magnitudes given here are strictly only valid for the similarity
measure and test sites used in this paper. Futher research is
needed for their validity outside the conditions described in this
study.
REFERENCES
Althof, R.J., Wind, M.G.J., & Dobbins, J.T., III (1997). A rapid
and automatic image registration algorithm with subpixel
accuracy. IEEE Transactions on Medical Imaging, 16, 308-316
Dodgson, N.A. (1992). Image resampling. London: University
of Cambridge Computer Laboratory
Haeberli, W., & Beniston, M. (1998). Climate change and its
impacts on glaciers and permafrost in the Alps. Ambio, 27,1
Haug, T., Kaab, A., & Skvarca, P. (2010). Monitoring ice shelf
velocities from repeat MODIS and Landsat data - a method
study on the Larsen C ice shelf, antarctic Peninsula, and 10
other ice shelves around Antarctica. The Cryosphere
Discussions (in review), 4, 35-75
Karybali, I.G., Psarakis, E.Z., Berberidis, K., & Evangelidis,
G.D. (2008). An efficient spatial domain technique for subpixel
image registration. Signal Processing: Image Communication,
23,711-724
Keys, R.G. (1981). Cubic convolution interpolation for digital
Image processing. IEEE transactions on acoustics, speech and
signal processing, 29, 1153-1160
Kaab, A., & Vollmer, M. (2000). Surface geometry, thickness
changes and flow fields on creeping mountain permafrost:
automatic extraction by digital image analysis. Permafrost and
Periglacial Processes, 11, 315-326
Lehmann, T.M., Gonner, C., & Spitzer, K. (1999). Survey:
interpolation methods in medical image processing. IEEE
Transactions on Medical Imaging, 18, 1049-1075
Lewis, J.P. (1995). Fast Normalized Cross-Correlation. Vision
Interface, 120-123
Nobach, H., & Honkanen, M. (2005). Two-dimensional
Gaussian regression for sub-pixel displacement estimation in
particle image velocimetry or particle position estimation in
particle tracking velocimetry. Experiments in Fluids, 38, 511-
515
Prasad, A., Adrian, R., Landreth, C., & Offutt, P. (1992). Effect
of resolution on the speed and accuracy of particle image
velocimetry interrogation. Experiments in Fluids, 13, 105-116
Rebetez, M., Lugon, R., & Baeriswyl, P.-A. (1997). Climatic
change and debris flows in high mountain regions: the case
study of the Ritigraben Torrent (Swiss Alps). Climatic Change,
36, 371-389
Scambos, T.A., Dutkiewicz, M.J., Wilson, J.C., &
Bindschadler, R.A. (1992). Application of image cross
correlation to the measurement of glacier velocity using satellite
image data. Remote Sensing of Environment, 42, 177-186
Toutin, T. (2004). Review article: Geometric processing of
remote sensing images: models, algorithms and methods.
International Journal of Remote Sensing, 25, 1893 - 1924
Westerweel, J. (1993). Digital particle image velocimetry:
theory and application. Delft: Delft University Press
Willert, C.E., & Gharib, M. (1991). Digital particle image
velocimetry. Experiments in Fluids, 10, 181-193
Yamaguchi, Y., Tanaka, S., Odajima, T., Kamai, T., &
Tsuchida, S. (2003). Detection of a landslide movement as
geometric misregistration in image matching of SPOT HRV
data of two different dates. International Journal of Remote
Sensing, 24, 3523 - 3534
Zhao, F., Huang, Q.M., & Gao, W. (2006). Image matching by
normalized cross-correlation. In, 31st IEEE International
Conference on Acoustics, Speech and Signal Processing (pp.
1977-1980). Toulouse, FRANCE
Zhou, P., & Goodson, K.E. (2001). Subpixel displacement and
deformation gradient measurement using digital image/speckle
correlation (DISC). Optical Engineering, 40, 1613-1620
Zitova, B., & Flusser, J. (2003). Image registration methods: a
survey. Image and Vision Computing, 21, 977-1000
ACKNOWLEDGEMENTS
The research was conducted at the Geosciences department of
the University of Oslo and financially supported by the
Norwegian Research Council (CORRIA project). The authors
are very grateful to both institutions.