Land Cover Detection with Unsupervised Clustering and Hierarchical Partitioning

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


An image segmentation technique relying on spatial clustering related to the single linkage approach has been put forward recently. This technique leads to a unique partition of the image domains into maximal segments satisfying a series of constraints related to local (α) and global (ω) intensity variation thresholds. The influence of such segmentation on clustering separability was assessed in this study, as well as the threshold values for segmentation maximising the cluster separability. The CLARA clustering method was used and the separability among clusters was calculated as the total separation between clusters. The clustering was applied to: (i) raw data; (ii) segmented data with varying α and ω parameters; and (iii) masked segmented data where the transition segments were excluded. The results show that the segmentation generally increases the separability of the clusters. The threshold parameters have an influence on the separability of clusters and maximising points could be identified while the transition segments were not completely included in one single cluster. The constrained connectivity paradigm could benefit land cover types/changes detection in the context of unsupervised object-oriented classification.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Brice, C., & Fennema, C. (1970). Scene analysis using regions. Artificial Intelligence, 1(3), 205–226.CrossRefGoogle Scholar
  2. Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3, 1–27.CrossRefMathSciNetGoogle Scholar
  3. Gower, J., & Ross, G. (1969). Minimum spanning trees and single linkage cluster analysis. Applied Statistics, 18(1), 54–64.CrossRefMathSciNetGoogle Scholar
  4. Haldiki, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17, 107–145.CrossRefGoogle Scholar
  5. Jardine, C., Jardine, N., & Sibson, R. (1967). The structure and construction of taxonomic hierarchies. Mathematical Biosciences, 1(2), 173–179.MATHCrossRefGoogle Scholar
  6. Jardine, N., & Sibson, R. (1971). Mathematical Taxonomy. London: Wiley.MATHGoogle Scholar
  7. Johnson, S. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241–254.CrossRefGoogle Scholar
  8. Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley.Google Scholar
  9. Nagao, M., Matsuyama, T. T., & Ikeda, Y. (1979). Region extraction and shape analysis in aerial photographs. Computer Graphics and Image Processing, 10(3), 195–223.CrossRefGoogle Scholar
  10. Soille, P. (2007). On genuine connectivity relations based on logical predicates. In 14th International Conference on Image Analysis and processing, Proceedings (pp. 487–492). 14th International Conference on Image Analysis and Processing, Modena, Italy, Sep 10–14.Google Scholar
  11. Soille, P. (2008). Constrained connectivity for hierarchical image partitioning and simplification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(7), 1132–1145.CrossRefGoogle Scholar
  12. Soille, P., & Grazzini, J. (2008). Advances in constrained connectivity. Lecture Notes in Computer Science, 4992, 423–433.CrossRefGoogle Scholar
  13. Vincent, L., & Soille, P. (1991). Watersheds in digital spaces – An efficient algorithm based on imemrsion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 583–598.CrossRefGoogle Scholar
  14. Zucker, S. (1976). Region growing: Childhood and adolescence. Computer Graphics and Image Processing, 5, 382–399.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.The Macaulay Land use Research InstituteAberdeenUK

Personalised recommendations