Vector Transition Classes Generation from Fuzzy Overlapping Classes

  • Enguerran Grandchamp
  • Sébastien Régis
  • Alain Rousteau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


We present in this paper a way to create transition classes and to represent them with vector structures. These classes are obtained using a supervised classification algorithm based on fuzzy decision trees. This method is useful to classify data which have a space evolution following a gradient such as forest, where transitions are spread over hundreds of meter, or other natural phenomenon. The vector representation is well adapted for integration in Geographical Information Systems because it is a more flexible structure than the raster representation. The method detailed takes into account local environmental conditions and leads to non regular gradient and fuzzy structures. It allows adding classes, called transition classes, when transition areas are too spread instead of fixing an arbitrary border between classes.


GIS decision tree fuzzy classification 


  1. 1.
    Altman, D.: Fuzzy set theoretic approaches for handling imprecision in spatial analysis. Internat. J. Geographical Inform. Systems 8(3), 271–289 (1994)CrossRefGoogle Scholar
  2. 2.
    Benz, U.C., et al.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry & Remote Sensing 5839– 258 (2004)Google Scholar
  3. 3.
    Burrough, P.A., Frank, A.U.: Geographic Objects with Indeterminate Boundaries, ch. 12, pp. 171–187. Taylor & Francis, London (1987)Google Scholar
  4. 4.
    Cleuziou, G.: OKM: une extension des k-moyennes pour la recherche de classes recouvrantes. In: 7èmes journées d’Extraction et de Gestion des Connaissances (EGC 2007), pp. 691–702 (2007)Google Scholar
  5. 5.
    Cleuziou, G.: An extended version of the k-means method for overlapping clustering. In: 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–4 (2008)Google Scholar
  6. 6.
    Cleuziou, G.: Two Variants of the OKM for Overlapping Clustering. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 292, pp. 149–166. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Cross, V.V.: Fuzzy extensions for relationships in a generalized object model. International Journal on Intelligent Systems 16, 843–861 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Fisher, P.: Sorites paradox and vague geographies. Fuzzy Sets and Systems 113, 7–18 (2000)CrossRefGoogle Scholar
  9. 9.
    Gama, J.: Functional Trees. In: Landwehr, N., Hall, M., Frank, E. (eds.) Logistic Model Trees (2005)Google Scholar
  10. 10.
    Grandchamp, E.: GIS information layer selection directed by remote sensing for ecological unit delineation. In: IGARSS (2009)Google Scholar
  11. 11.
    Grandchamp, E.: Raster-vector cooperation algorithm for GIS. In: GeoProcessing (2010)Google Scholar
  12. 12.
    Kainz: Introduction to Fuzzy Logic and Applications in GIS – Example (2011)Google Scholar
  13. 13.
    Mukhopadhyay, B.: Integrating exploration dataset in GIS using fuzzy inference modeling, GISdevelopment Google Scholar
  14. 14.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)Google Scholar
  15. 15.
    Raines, G.L., et al.: New fuzzy logic tools in ArcGIS 10. ESRI Communication (2010)Google Scholar
  16. 16.
    Rousteau, A.: Carte écologique de la Guadeloupe. 3 feuilles au 1/75.000ème et notice (36 p.). Conseil Général de la Guadeloupe, Office National des Forêts et Parc National de la Guadeloupe (1996)Google Scholar
  17. 17.
    Sawatzky, D., Raines, G.L., Bonham-Carter, G.: Spatial Data Modeller. Technical Report (2008)Google Scholar
  18. 18.
    Schneider, M.: Spatial Data Types for Database Systems, Finite Resolution Geometry for Geographic Information Systems. LNCS, vol. 1288, 275 p. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  19. 19.
    Schneider, M.: Uncertainty Management for Spatial Data in Databases: Fuzzy Spatial Data Types. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 330–351. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Sunila, R., Horttanainen, P.: Fuzzy Model of Soil Polygons for Managing the Imprecision Interfacing GeoStatistics and GIS (2009)Google Scholar
  21. 21.
    Zhu, A.X., et al.: Soil Mapping Using GIS. Expert Knowledge, and Fuzzy Logic. Simonson, Soil Sci. Soc. Am. J. 65, 1463–1472 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Enguerran Grandchamp
    • 1
  • Sébastien Régis
    • 1
  • Alain Rousteau
    • 2
  1. 1.LAMIA LaboratoryFrench West Indies UniversityGuadeloupeFrance
  2. 2.DUNECAR LaboratoryFrench West Indies UniversityGuadeloupeFrance

Personalised recommendations