Change and Motion Estimation

  • Valliappa Lakshmanan
Part of the Geotechnologies and the Environment book series (GEOTECH, volume 6)


In this chapter, we examine techniques to estimate motion and change from a sequence of spatial grids when what is being observed is moving as well as evolving. We consider first simply subtracting successive grids and point out the limitations of this approach. Then, we consider using partial derivatives (optical flow) which is suitable for fluid-like flows. Cross-correlation is often better than using partial derivatives when the amount of change and movement are large. We examine a way to improve cross-correlation, by performing it in the frequency domain (phase correlation). Then, we discuss object tracking which is suitable when the spatial grid consists of objects that are moving and changing rather than of fluid-like flows. Object tracking involves associating objects between frames, and this leads us to a discussion of the Hungarian method. Finally, we point out that a hybrid approach allows us to retain the advantages of both object tracking and cross-correlation while side-stepping their disadvantages. We finish the chapter by discussing different ways of computing temporal attributes from spatial grids.


Optical Flow Motion Vector Motion Estimate Object Tracking Current Frame 
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.


  1. Barnes C, Fritz H, Yoo J (2007) Hurricane disaster assessments with image driven data mining in high resolution satellite imagery. IEEE Trans Geosci Remote Sens 45(6):1631–1640CrossRefGoogle Scholar
  2. Bourgeois F, Lassalle J (1971) An extension of the Munkres algorithm for the assignment problem to rectangular matrices. Commun ACM 14(12):802–804CrossRefGoogle Scholar
  3. Brown RG, Hwang P (1997) Introduction to random signals and applied Kalman filtering. Wiley, New YorkGoogle Scholar
  4. Daubechies I (1992) Ten Lectures on wavelets, 1st edn. SIAM, PhiladelphiaCrossRefGoogle Scholar
  5. De Castro E, Morandi C (1987) Registration of translated and rotated images using finite fourier transforms. IEEE Trans Pattern Anal Mach Intell 9:700–703CrossRefGoogle Scholar
  6. Dixon M, Wiener G (1993) TITAN: thunderstorm identification, tracking, analysis and nowcasting – a radar-based methodology. J Atmos Ocean Tech 10:785–797CrossRefGoogle Scholar
  7. Fraser R, Abuelgasim A, Latifovic R (2005) A method for detecting large-scale forest cover change using coarse spatial resolution imagery. Remote Sens Environ 95(4):414–427CrossRefGoogle Scholar
  8. Horn P, Schunck B (1981) Determining optical flow. Artif Intell 17:185–203CrossRefGoogle Scholar
  9. Johnson JT, MacKeen P, Witt A, Mitchell ED, Stumpf G, Eilts M, Thomas K (1998) The storm cell identification and tracking algorithm: an enhanced WSR-88D algorithm. Weather Forecast 13(6):263–276CrossRefGoogle Scholar
  10. Lakshmanan V, Rabin R, DeBrunner V (2003) Multiscale storm identification and forecast. J Atmos Res 67:367–380CrossRefGoogle Scholar
  11. Michel R, Rignot E (1999) Flow of Moreno Glaciar, Argentina, from repeat-pass shuttle imaging radar images: comparison of the phase correlation method with radar interferometry. J Glaciol 45:93–100Google Scholar
  12. Rudlosky S (2010) Assessing storm severity using lightning and radar information. PhD thesis, Florida State UniversityGoogle Scholar
  13. Scambos TA, Dutkiewicz MJ, Wilson JC, Bindschadler RA (1992) Application of image cross-correlation to the measurement of glacier velocity using satellite image data. Remote Sens Environ 42(3):177–186CrossRefGoogle Scholar
  14. Singh A (1989) Review article: digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht. 2012

Authors and Affiliations

  • Valliappa Lakshmanan
    • 1
  1. 1.National Weather CenterUniversity of OklahomaNormanUSA

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