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Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

  • Eva Vidal-Fernández
  • Jesús M. Almendros-JiménezEmail author
  • José A. Piedra
  • Manuel Cantón
Chapter
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Abstract

The processing of ocean satellite images has as goal the detection of phenomena related with ocean dynamics. In this context, Mesoscale Oceanic Structures (MOS) play an essential role. In this chapter we will present the tool developed in our group in order to extract knowledge rules for the retrieval of MOS in ocean satellite images. We will describe the implementation of the tool: the workflow associated with the tool, the user interface, the class structure, and the database of the tool. Additionally, the experimental results obtained with the tool in terms of fuzzy knowledge rules as well as labeled structures with these rules are shown. These results have been obtained with the tool analyzing chlorophyll and temperature images of the Canary Islands and North West African coast captured by the SeaWiFS and MODIS-Aqua sensors.

Keywords

Remote sensing Satellite images Mesoscale oceanic structures Image processing Tools SeaWiFS MODIS-Aqua 

Notes

Acknowledgments

This work was funded by the EU ERDF and the Spanish Ministry of Economy and Competitiveness (MINECO) under Projects TIN2013-41576-R, TIN2013-44742-C4-4-R and CGL2013-48202-C2-2-R, and the Andalusian Regional Government (Spain) under Project P10-TIC-6114. This work also received funding from the CEiA3 and CEIMAR consortiums.

References

  1. 1.
    Bakun, A.: Global climate change and intensification of coastal ocean upwelling. Science 247(4939), 198–201 (1990)CrossRefGoogle Scholar
  2. 2.
    Change, I.P.O.C.: Climate change 2013: the physical science basis. Agenda 6(07), 333 (2013)Google Scholar
  3. 3.
    McGregor, H., Dima, M., Fischer, H., Mulitza, S.: Rapid 20th-century increase in coastal upwelling off northwest Africa. Science 315(5812), 637–639 (2007)CrossRefGoogle Scholar
  4. 4.
    Gregg, W.W., Conkright, M.E., Ginoux, P., O’Reilly, J.E., Casey, N.W.: Ocean primary production and climate: global decadal changes. Geophys. Res. Lett 30(15), 1809 (2003)CrossRefGoogle Scholar
  5. 5.
    Angel, M.V., Fasham, M.J.R.: Eddies and biological processes. Eddies in Marine Science, pp. 492–524. Springer, Berlin (1983)Google Scholar
  6. 6.
    Rubino, A.: Fluctuating mesoscale frontal features: structures and manifestations in the real ocean. Kumulative Habilitationsschrift, Universitat Hamburg (2005)Google Scholar
  7. 7.
    Birkhoff, G., et al.: Jets, Wakes, and Cavities. Elsevier, Amsterdam (2012)Google Scholar
  8. 8.
    Chelton, D.B., Schlax, M.G., Samelson, R.M., de Szoeke, R.A.: Global observations of large oceanic eddies. Geophys. Res. Lett. 34(15), L15606 (2007)Google Scholar
  9. 9.
    Robinson, I.S.: Ocean mesoscale features: upwelling and other phenomena. Discovering the Ocean from Space, pp. 159–193. Springer, Berlin (2010)Google Scholar
  10. 10.
    Schwartz, M.: Encyclopedia of Coastal Science. Springer Science & Business Media, New York (2006)Google Scholar
  11. 11.
    Zhang, Z., Zhang, Y., Wang, W., Huang, R.X.: Universal structure of mesoscale eddies in the ocean. Geophys. Res. Lett. 40(14), 3677–3681 (2013)CrossRefGoogle Scholar
  12. 12.
    Barton, E.D., Arístegui, J., Tett, P., Cantón, M., García-Braun, J., Hernández-León, S., Nykjaer, L., Almeida, C., Almunia, J., Ballesteros, S., Basterretxea, G., Escánez, J., García-Weil, L., Hernández-Guerra, A., López-Laatzen, F., Molina, R., Montero, M.F., Navarro-Pérez, E., Rodríguez, J.M., van Lenning, K., Vélez, H., Wild, K.: The transition zone of the Canary Current upwelling region. Progress Oceanogr. 41(4), 455–504 (1998)CrossRefGoogle Scholar
  13. 13.
    Kersalé, M., Doglioli, A., Petrenko, A.: Sensitivity study of the generation of mesoscale eddies in a numerical model of Hawaii islands. Ocean Sci. 7(3), 277–291 (2011)CrossRefGoogle Scholar
  14. 14.
    Lorenzo, E.D., Miller, A.J., Neilson, D.J., Cornuelle, B.D., Moisan, J.R.: Modelling observed California Current mesoscale eddies and the ecosystem response. Int. J. Remote Sens. 25(7–8), 1307–1312 (2004)CrossRefGoogle Scholar
  15. 15.
    Lumpkin, C.F.: Eddies and currents of the Hawaiian Islands. Ph.D. thesis, University of Hawaii (1998)Google Scholar
  16. 16.
    Oke, P.R., Griffin, D.A.: The cold-core eddy and strong upwelling off the coast of New South Wales in early 2007. Deep Sea Res. Part II: Top. Stud. Oceanogr. 58(5), 574–591 (2011)CrossRefGoogle Scholar
  17. 17.
    Meunier, T., Barton, E.D., Barreiro, B., Torres, R.: Upwelling filaments off Cap Blanc: interaction of the NW African upwelling current and the Cape Verde frontal zone eddy field. J. Geophys. Res.: Oceans (1978–2012) 117, C8 (2012)Google Scholar
  18. 18.
    Tejera, A., García-Weil, L., Heywood, K., Cantón-Garbín, M.: Observations of oceanic mesoscale features and variability in the Canary Islands area from ERS-1 altimeter data, satellite infrared imagery and hydrographic measurements. Int. J. Remote Sens. 23(22), 4897–4916 (2002)CrossRefGoogle Scholar
  19. 19.
    Sangra, P., Pelegrí, J., Hernández-Guerra, A., Arregui, I., Martín, J., Marrero-Díaz, A., Martínez, A., Ratsimandresy, A., Rodríguez-Santana, A.: Life history of an anticyclonic eddy. J. Geophys. Res. 110(C3), C03,021 (2005)Google Scholar
  20. 20.
    Arístegui, J., Sangra, P., Hernández-León, S., Cantón, M., Hernández-Guerra, A., Kerling, J.: Island-induced eddies in the Canary islands. Deep Sea Res. Part I: Oceanogr. Res. Pap. 41(10), 1509–1525 (1994)CrossRefGoogle Scholar
  21. 21.
    Baatz, M., Hoffmann, C., Willhauck, G.: Progressing from object-based to object-oriented image analysis. Object-Based Image Analysis, pp. 29–42. Springer, Berlin (2008)Google Scholar
  22. 22.
    Musci, M., Feitosa, R.Q., Costa, G.A.: An object-based image analysis approach based on independent segmentations. In: Urban Remote Sensing Event (JURSE), 2013 Joint, pp. 275–278. IEEE (2013)Google Scholar
  23. 23.
    Drăguţ, L., Blaschke, T.: Automated classification of landform elements using object-based image analysis. Geomorphology 81(3), 330–344 (2006)Google Scholar
  24. 24.
    Jovanovic, D., Govedarica, M., Dordevic, I., Pajic, V.: Object based image analysis in forestry change detection. In: 2010 8th International Symposium on Intelligent Systems and Informatics (SISY), pp. 231–236. IEEE (2010)Google Scholar
  25. 25.
    Rastner, P., Bolch, T., Notarnicola, C., Paul, F.: A comparison of pixel-and object-based glacier classification with optical satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(3), 853–862 (2014)Google Scholar
  26. 26.
    Ko, B., Byun, H.: Frip: a region-based image retrieval tool using automatic image segmentation and stepwise Boolean AND matching. IEEE Trans. Multimed. 7(1), 105–113 (2005)CrossRefGoogle Scholar
  27. 27.
    Shrivastava, N., Tyagi, V.: A review of roi image retrieval techniques. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, pp. 509–520. Springer, Berlin (2015)Google Scholar
  28. 28.
    Vidal-Fernández, E., Piedra, J.A., Almendros-Jiménez, J.M., Cantón, M.: A location based approach to classification of mesoscale oceanic structures in SeaWiFS and MODIS-Aqua images from the North West Africa Area. Int. J. Remote Sens. 36(24), 6135–6159 (2015)CrossRefGoogle Scholar
  29. 29.
    Vidal-Fernández, E., Piedra, J.A., Almendros-Jiménez, J.M., Cantón, M.: OBIA system for identifying mesoscale oceanic structures in SeaWiFS and MODIS-aqua images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(3), 1256–1265 (2015)Google Scholar
  30. 30.
    Almendros-Jiménez, J.M., Domene, L., Piedra-Fernández, J.A.: A framework for ocean satellite image classification based on ontologies. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(2), 1048–1063 (2013)CrossRefGoogle Scholar
  31. 31.
    Piedra-Fernandez, J.A., Cantón-Garbín, M., Wang, J.Z.: Feature selection in AVHRR ocean satellite images by means of filter methods. IEEE Trans. Geosci. Remote Sens. 48(12), 4193–4203 (2010)CrossRefGoogle Scholar
  32. 32.
    Piedra-Fernández, J.A., Ortega, G., Wang, J.Z., Cantón-Garbín, M.: Fuzzy content-based image retrieval for oceanic remote sensing. IEEE Trans. Geosci. Remote Sens. 52(9), 5422–5431 (2014)Google Scholar
  33. 33.
    NASA: Ocean Color Web. http://oceancolor.gsfc.nasa.gov (2013)
  34. 34.
    Liu, Z., Hou, Y.: Kuroshio front in the East China Sea from Satellite SST and remote sensing data. IEEE Geosci. Remote Sens. Lett. 9(3), 517–520 (2012)CrossRefGoogle Scholar
  35. 35.
    Marcello, J., Marques, F., Eugenio, F.: Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 43(7), 1605–1616 (2005)CrossRefGoogle Scholar
  36. 36.
    Mityagina, M., Lavrova, O.: Dynamic phenomena in the coastal waters of the north-eastern black sea retrieved from satellite data. In: IEEE International Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008, vol. 2, pp. II–347. IEEE (2008)Google Scholar
  37. 37.
    Patel, S., Balasubramanian, R., Gangopadhyay, A.: Automatic detection of oceanic eddies in SeaWiFS-derived color images using neural networks and shape analysis. Proc. IEEE IGARSS 2, II–835–II–838 (2008)Google Scholar
  38. 38.
    Xiao, B., Hu, S., Qiang, X.: Research on the ocean primary production pattern based remote sensing. In: 2010 International Conference on Audio Language and Image Processing (ICALIP), pp. 1543–1546. IEEE (2010)Google Scholar
  39. 39.
    Sathyendranath, S., Brewin, B., Mueller, D., Doerffer, R., Krasemann, H., Mélin, F., Brockmann, C., Fomferra, N., Peters, M., Grant, M., et al.: Ocean colour climate change initiative–approach and initial results. In: IEEE International and Geoscience and Remote Sensing Symposium (IGARSS), pp. 2024–2027. IEEE (2012)Google Scholar
  40. 40.
    Saulquin, B., Gohin, F., Garrello, R.: Regional objective analysis for merging high-resolution MERIS, MODIS/Aqua, and SeaWiFS chlorophyll-a data from 1998 to 2008 on the European Atlantic shelf. IEEE Trans. Geosci. Remote Sens. 49(1), 143–154 (2011)CrossRefGoogle Scholar
  41. 41.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)Google Scholar
  42. 42.
    Maitra, S.: Moment invariants. Proc. IEEE 67, 697–699 (1979)CrossRefGoogle Scholar
  43. 43.
    Galvez, J.M., Cantón, M.: Normalization and shape recognition of three-dimensional objects by 3d moments. Pattern Recognit. 26(5), 667–681 (1993)CrossRefGoogle Scholar
  44. 44.
    Teague, M.R.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70, 920–930 (1980)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Zunic, J., Sladoje, N.: Efficiency of characterizing ellipses and ellipsoids by discrete moments. IEEE Trans. Pattern Anal. Mach. Intell. 22(4), 407–414 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Eva Vidal-Fernández
    • 1
  • Jesús M. Almendros-Jiménez
    • 1
    Email author
  • José A. Piedra
    • 1
  • Manuel Cantón
    • 1
  1. 1.Department of InformaticsUniversity of AlmeríaAlmeríaSpain

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