Parallel Algorithms of Discrete Fourier Transform for Earth Surface Modeling

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


One of the important problems in the use of remote sensing from satellites is three-dimensional modeling of surface—fragments both dynamic (e.g., ocean surface) and slowly varying ones. Some researchers propose the use of methods that are based on the discrete Fourier transform (DFT). For such an approach in the dynamic case, the time of transform implementation is critical. By decreasing this length of time, one can increase the modeled fragment size, thereby improving quality and model authenticity. Multiprocessor systems and graphic processors allow the use of special technology for parallel computations to decrease implementation time. This chapter presents the published approaches, as well as the author's approach, to DFT parallel algorithms for this particular problem.


Remote sensing 3D modeling Discrete fourier transform Parallel algorithms 



This research was financially supported by the Russian Foundation for Basic Research, project No. 12-07-00751, 12-01-00822, 11-07-12060, 11-07-12059.


  1. Aliev MV (2002) Fast algorithms of d-dimensional DFT of real signal in commutative-associative algebras of 2d dimensionality over the real number field (in Russian). Comput Opt 24:130–136Google Scholar
  2. Chernov VM (1993) Fast algorithms of discrete orthogonal transforms for data represented in cyclotomic fields. Pattern Recogn Image Anal 3(4):455–458Google Scholar
  3. Chicheva MA (2011) Parallel computation of multidimensional discrete orthogonal transforms reducible to a discrete fourier transform. Pattern Recogn Image Anal 21(3):381–383CrossRefGoogle Scholar
  4. Finch M (2004) Effective water simulation from physical models. In: Fernando R (ed) GPU gems: programming techniques, tips and tricks for real-time graphics. Addison-Wesley, Reading, pp 5–29Google Scholar
  5. Floriania LD, Montanib C, Scopignoc R (1994) Parallelizing visibility computations on triangulated terrains. Int J Geogr Inf Syst 8(6):515–531CrossRefGoogle Scholar
  6. Frejlichowski D (2011) A three-dimensional shape description algorithm based on polar-fourier transform for 3D model retrieval. In: SCIA’11 proceedings of the 17th scandinavian conference on image analysis. Springer, Berlin, pp 457–466Google Scholar
  7. Guan Q, Kyriakidis PC, Goodchild M (2011) A parallel computing approach to fast geostatistical areal interpolation. Int J Geogr Inf Sci 25(8):1241–1267 Special Issue on Data-Intensive Geospatial ComputingCrossRefGoogle Scholar
  8. Henry D (2008) On gerstner’s water wave. J Nonlinear Math Phys 15(2):87–95CrossRefGoogle Scholar
  9. Honec J, Honec P, Petyovsky P, Valach S, Brambor J (2001) Parallel 2-D FFT implementation with DSPs. In: 13th international conference on process control, Strbske Pleso, SlovakiaGoogle Scholar
  10. Isidoro J, Vlachos A, Brennan C (2002) Effective water simulation from physical models. Rendering ocean water. Wordware Publishing, Inc., Plano, pp 347–356Google Scholar
  11. Noskov MV, Tutatchikov VS (2011) About calculation of two-dimensional fast fourier transform. In: 8th open German-Russian workshop “pattern recognition and image understanding” (OGRW-8-2011), Nizhny Novgorod, pp 219–221Google Scholar
  12. Takahashi D (2003) E–cient implementation of parallel three-dimensional FFT on clusters of PCs. Comput Phys Commun 152:144–150CrossRefGoogle Scholar
  13. Tessendorf J (2009) Simulating ocean water. In: ACM SIGGRAPH course notes #9 (simulating nature: realistic and interactive techniques). ACM Press, New YorkGoogle Scholar
  14. Widenera MJ, Cragob NC, Aldstadta O (2012) Developing a parallel computational implementation of AMOEBA. Int J Geogr Inf Sci 26(9):1707–1723CrossRefGoogle Scholar
  15. Yinab L, Shawac S-L, Wangd D, Carre EA, Berryf MW, Grossg LJ, Comiskeye EJ (2012) A framework of integrating GIS and parallel computing for spatial control problems—a case study of wildfire control. Int J Geogr Inf Sci 26(4):621–641CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Image Processing System Institute of RASSamaraRussian Federation

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