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A New Model for Cloud Tracking and Analysis on Satellite Images

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Abstract

The study of convective clouds is an important issue in weather analysis. Previous methods are based on shape matching and level set. In this paper, a method based on snake model is used for cloud tracking. Snakes are known to be more efficient than level set for contour detection however they do not handle topological changes. Therefore, geometrical criteria are introduced to characterize topological transformations. Geometrical techniques are then combined and inserted in the tracking algorithm to perform morphological operations. By applying this method, a history of the positions of the clouds is obtained. In a second stage, a data model is presented for cloud interrogation. Physical information is introduced and data are organized so that spatiotemporal queries can be performed. Results obtained with the tracking method on a real data set are presented and some query examples are given.

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References

  1. M.P. Armstrong. “Temporality in spatial databases,” in Proc. GIS/LIS’88, pp. 2:880–889, 1988.

  2. Y. Arnaud, M. Desbois, and J. Maizi. “Automatic tracking and characterization of African convective systems on meteosat pictures,” Journal of Applied Meteorology, Vol. 31:443–453, 1992.

    Article  Google Scholar 

  3. Blake and M. Isard. Active Contours. Springer: Berlin Heidelberg New York, 1998.

    Google Scholar 

  4. T. Cheng and M. Molenaar. “A process-oriented spatio-temporal data model to support physical environmental modeling,” in Proceedings of the 8th International Symposium on Spatial Data Handling, pp. 418–430, 1998.

  5. C. Claramunt and M. Thériault. “Managing time in GIS: An event oriented approach,” in J. Cliford and A. Atuzhilin (Eds.), Recent Advances on Temporal Databases, 23–42, 1995.

  6. L. Cohen and I. Cohen. “Finite element methods for active contours models and ballons for 2D and 3D images,” IEEE Pattern Analysis and Machine Intelligence, Vol. 15:1131–1147, 1993.

    Article  Google Scholar 

  7. H. Delingette and J. Montagnat. “Topology and shape constraints on parametric active contours,” INRIA, Tech. Rep. 3880, 2000.

  8. M. Erwig, R.H. Güting, M. Schneider, and M. Vazirgiannis. “Spatio-temporal data types: An approach to modeling and querying moving objects in databases,” GeoInformatica, Vol. 3:265–291, 1999.

    Article  Google Scholar 

  9. J. Foley, A. van Dam, S. Feiner, J. Hugues, and R. Phillips. Computer Graphics in C: Principle and Practice. 2nd edition, Addison-Wesley: Reading, MA, 1996.

    Google Scholar 

  10. H. Freeman. “Computer processing of line-drawing image,” Computing Surveys, Vol. 6:57–97, 1974.

    Article  Google Scholar 

  11. M. Kass, A. Witkin, and D. Terzopoulos. “Snakes: Active contour models,” in Proceedings of International Conference on Computer Vision, pp. 259–268, 1987.

  12. G. Langran and N.R. Christman. “A framework for temporal geographic information,” Cartographica, Vol. 25:1–14, 1988.

    Google Scholar 

  13. L.D. Montgomery. “Temporal Geographic Information Systems Technology and Requirements: Where We are Today,” PhD thesis, Department of Geography, Ohio State University, 1995.

  14. D. Mukherjee and S. Acton. “Cloud tracking by scale space classification,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 40:405–415, 2002.

    Article  Google Scholar 

  15. C. Papin, P. Bouthemy, E. Mémin, and G. Rochard. “Tracking and characterization of highly deformable cloud structure,” in European Conference on Computer Vision 2, pp. 428–442, 2000.

  16. D. Peuquet and N. Duan. “An Event-Based Spatio-Temporal Data Model (ESTDM) for temporal analysis of geographical data,” International Journal of Geographical Information Systems, Vol. 9:7–24, 1995.

    Article  Google Scholar 

  17. J. Sethian. Level Set Methods. Cambridge University Press: Cambridge, UK, 1996.

    Google Scholar 

  18. C. Xu and J. Prince. “Snakes, shapes and gradient vector flow,” IEEE Transactions on Image Processing, Vol. 7:359–369, 1998.

    Article  Google Scholar 

  19. Y. Yang, H. Lin, Z. Guo, Z. Fang, and J. Jiang. “Automatic tracking and characterization of multiple moving clouds in satellite images,” in International Conference on Systems, Man and Cybernetics, pp. 3088–3093, 2004.

  20. M. Yuan. “Wildfire conceptual modeling for building GIS space–time model,” in A.U. Frank, W. Kuhn, and P. Haunold (Eds.), Temporal Data in Geographic Information Systems, 47–56, 1995.

  21. M. Yuan and J. McIntosh. “A typology of spatiotemporal information queries,” in K. Shaw, R. Ladner, and M. Abdelguerfi (Eds.), Mining Spatiotemporal Information Systems, 63–82, 2002.

  22. L. Zhou, C. Kambhamettu, and D. Goldof. “Fluid structure and motion analysis from multi-spectrum 2D cloud image sequences,” in Proceedings of Conference on Computer Vision and Pattern Recognition, pp. II 744–751, 2000.

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Acknowledgements

This research has been founded by the RGC grant from Hong Kong Research Grant Council under grant number CUHK4132/99H. The authors would like to thank Dr Tao Cheng whose advices were much helpful for the achievement of this work and David Guilbert for his valuable help on the realization of the model with Arcview.

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Correspondence to Eric Guilbert.

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Guilbert, E., Lin, H. A New Model for Cloud Tracking and Analysis on Satellite Images. Geoinformatica 11, 287–309 (2007). https://doi.org/10.1007/s10707-006-0008-6

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  • DOI: https://doi.org/10.1007/s10707-006-0008-6

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