Full text: Papers accepted on the basis of peer-reviewed abstracts (Part B)

In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
186 
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.
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.