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Tracking of sea and air flows from sequential satellite images by the relaxation-contour method

  • Proceedings of 7th International Conference on Pattern Recognition And Image Analysis: New Information Technologies St. Petersburg, Russian Federation, October 18–23, 2004
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Abstract

A method for visualizing the dynamics of water and air masses in the form of velocity fields obtained from the time series of satellite images is proposed. The method is based on the contour approach to the selection of inhomogeneities to be traced (tracers). The main criterion for choosing a tracer is the shape of the contour (geometrical context). The procedure applied for the optimal tracer selection is based on the method of relaxation labeling. The efficiency of the proposed method is estimated by comparison with that of the traditional cross-correlation approach.

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References

  1. V. A. Bobkov, A. V. Kazanskii, and M. A. Morozov, “Vague Contour Detection as Applied to Determination of Current Velocity from Satellite Images,” Avtometriya, No. 2, 3–12 (2001).

  2. V. A. Bobkov, A. V. Kazanskii, M. A. Morozov, and S. V. Mel’man, “Determination of the Water-Vapor Wind Vectors from Geostationary Satellite Data: Comparison between Correlation and Contour Methods,” Issled. Zemli iz Kosmosa, No. 3, 52–59 (2004).

  3. Cho-Huak Teh and Roland T. Chin, “On the Detection of Dominant Points on Digital Curves,” IEEE Trans. Pattern Anal. Mach. Intell. 11(8), (1989).

  4. W. J. Emery, A. C. Thomas, M. J. Collins, et al., “An Objective Method for Computing Advective Surface Velocities from Sequential Infrared Satellite Images,” J. Geophys. Res. 91(C11), 12865–12878 (1986).

    Article  Google Scholar 

  5. J. Gao and M. B. Lythe, “Effectiveness of the MCC Method in Detecting Oceanic Circulation Patterns at a Local Scale from Sequential AVHRR Images,” Photogramm. Eng. Remote Sens. 64(4), 301–308 (1998).

    Google Scholar 

  6. K. Holmlund, “Half Hourly Wind Data from Satellite Derived Water Vapor Measurements,” Adv. Space Res. 16(10), 59–68 (1995).

    Article  Google Scholar 

  7. K. Holmlund, “Current Status of EUMETSAT Operational and Future AMV Extraction Facilities,” in Proc, of the 6th Int, Winds Workshop, Madison, 2002, pp. 45–52.

  8. W. P. Menzel, “Cloud Tracking with Satellite Imagery: from the Pioneering Work of Ted Fujita to the Present,” Bull. Amer. Meteorol. Soc. 82, 33–47 (2001).

    Article  Google Scholar 

  9. D. P. Mukherjee and S. T. Acton, “Cloud Tracking by Scale Space Classification,” IEEE Trans. Geosci. Remote Sens. GE-40, 405–415 (2002).

    Article  Google Scholar 

  10. S. Nieman, W. P. Menzel, C. Hayden, et al., “Fully Automated Cloud-Drift Winds in NESDIS Operations,” Bull. Am. Meteorol. Soc. 78, 1121–1133 (1997).

    Article  Google Scholar 

  11. R. M. Ninnis, W. J. Emery, and M. J. Collins, “Automated Extraction of Pack Ice Motion from Advanced Very High Resolution Radiometry Imagery,” J. Geophys. Res. 91, 10725–10734 (1986).

    Article  Google Scholar 

  12. A. Rosenfeld, R. A. Hummel, and S. W. Zucker, “Scene Labeling by Relaxation Operations,” IEEE Trans. Syst. Man, and Cybern. 6, 420–433 (1976).

    Article  MathSciNet  MATH  Google Scholar 

  13. J. V. Vesecky, R. Samadani, M. P. Smith, et al., “Observation of Sea-Ice Dynamics Using Synthetic Aperture Radar Images: Automated Analysis,” IEEE Trans. Geosci. Remote Sens. GE-26, 38–48 (1988).

    Article  Google Scholar 

  14. Q. X. Wu, D. Pariman, S. J. McNeil, and E. J. Barness, “Computing Advective Velocities from Satellite Images of Sea Surface Temperature,” IEEE Trans. Geosci. Remote Sens. GE-30, 166–175 (1992).

    Article  Google Scholar 

  15. Q. X. Wu, “A Correlation Relaxation Labeling Framework for Computing Optical Flow Template Matching from a New Perspective”, IEEE Trans. Pattern Anal. Mach. Intell. 17, 843–853 (1995).

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Automation and Control Processes, Far East Division, Russian Academy of Sciences, ul. Radio 5, Vladivostok, 690041, Russia

    M. A. Morozov

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  1. M. A. Morozov
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Correspondence to M. A. Morozov.

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Mikhail A. Morozov. Born 1972. Graduated from the Far East State Technical University in 1996 as an electronic engineer. From 1996, Junior Researcher at the Institute of Automation and Control Processes, Far East Division, Russian Academy of Sciences. Scientific interests: image processing and recognition, satellite monitoring, geographic information systems, computer graphics. Author of 7 publications.

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Morozov, M.A. Tracking of sea and air flows from sequential satellite images by the relaxation-contour method. Pattern Recognit. Image Anal. 18, 107–111 (2008). https://doi.org/10.1134/S1054661808010124

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  • DOI: https://doi.org/10.1134/S1054661808010124

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