Abstract
In this paper some insights into the behavior of interpolation functions for resampling high resolution satellite images are presented. Using spatial and frequency domain characteristics, splines interpolation performance is compared to nearest-neighbor, linear and cubic interpolation. It is shown that splines interpolation injects spatial information into the final resample image better than the other three methods. Splines interpolation is also shown to be faster than cubic interpolation when the former is implemented with the LU decomposition algorithm for its tridiagonal system of linear equations. Therefore, if the main purpose for high resolution satellite resampling is to obtain an optimal smooth final image, intuitive and experimental justifications are provided for preferring splines interpolation to nearest-neighbor, linear and cubic interpolation.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lehmann, T.M., Gönner, C., Spitzer, K.: Survey: Interpolation Methods in Medical Image Processing. IEEE Transaction on Medical Imaging 18(11), 1049–1075 (1999)
Thévenaz, P., Blu, T., Unser, M.: Interpolation Revisited. IEEE Transactions on Medical Imaging 19(7), 739–758 (2000)
Unser, M., Aldroubi, A., Eden, M.: Fast B-spline transforms for continuous image representation and interpolation. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 277–285 (1991)
Bartels, R., Beatty, J., Barsky, B.: An Introduction to Splines for Use in Computer Graphics and Geometric Modeling, 476 pages. Elsevier, Amsterdam (1987)
Unser, M., Blu, T.: Cardinal exponential splines: part I - theory and filtering algorithms. IEEE Transactions on Signal Processing 53(4), 1425–1438 (2005)
Unser, M.: Cardinal exponential splines: part II - think analog, act digital. IEEE Transactions on Signal Processing 53(4), 1439–1449 (2005)
Key, R.G.: Cubic Convolution Interpolation for Digital Image Processing. IEEE Transaction on Acoustics, Speech, and Signal Processing 29(6), 1153–1160 (1981)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++. The Art of Scientific Computing, 2nd edn., pp. 126–132. Cambridge University Press, Cambridge (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Malpica, J.A. (2005). Splines Interpolation in High Resolution Satellite Imagery. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_68
Download citation
DOI: https://doi.org/10.1007/11595755_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30750-1
Online ISBN: 978-3-540-32284-9
eBook Packages: Computer ScienceComputer Science (R0)