Hierarchical clustering algorithms for segmentation of multispectral images

  • I. A. Pestunov
  • S. A. Rylov
  • V. B. Berikov
Analysis and Synthesis of Signals and Images


Computationally efficient HCA and HECA hierarchical clustering algorithms for segmentation of multispectral images have been developed using the grid and ensemble approaches. A special metric is proposed to identify embedded clusters even in the presence of overlapping. The efficiency of the algorithms has been confirmed by the results of experimental studies using model and real data.


ensemble hierarchical clustering algorithm grid approach segmentation of multispectral satellite images 


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  1. 1.
    R. C. Gonzalez and R. E. Woods, Digital Image Processing (Tekhnosphera, Moscow, 2006) [in Russian].Google Scholar
  2. 2.
    P. A. Chochia, “Image Segmentation Based on the Analysis of Distances in an Attribute Space,” Avtometriya 50 (6), 97–110 (2014) [Optoelectron., Instrum. Data Process. 50 (6), 613–624 (2014)].Google Scholar
  3. 3.
    I. A. Pestunov and Yu. N. Sinyavskii, “Clustering Algorithms in Problems of Segmentation of Satellite Images,” Vestn. KemGU 2 (4(52)), 110–125 (2012).Google Scholar
  4. 4.
    R. Xu and D. I. Wunsch, “Survey of Clustering Algorithms,” IEEE Trans. Neural Networks 16 (3), 645–678 (2005).CrossRefGoogle Scholar
  5. 5.
    A. K. Jain, “Data Clustering: 50 years Beyond K-Means,” Patt. Recogn. Lett. 31 (8), 651–666 (2010).CrossRefGoogle Scholar
  6. 6.
    R. Ghaemi, M. Sulaiman, H. Ibrahim, and N. Mustapha, “A Survey: Clustering Ensembles Techniques,” World Acad. Sci., Eng. Technol. 3 (2), 535–544 (2009).Google Scholar
  7. 7.
    P. Hope, L. Hall, and D. Goldgof, “A Scalable Framework for Cluster Ensembles,” Patt. Recogn. 42 (5), 676–688 (2009).CrossRefGoogle Scholar
  8. 8.
    R. Kashef and M. Kamel, “Cooperative Clustering,” Patt. Recogn. 43 (7), 2315–2329 (2010).zbMATHCrossRefGoogle Scholar
  9. 9.
    J. Jia, B. Liu, and L. Jiao, “Soft Spectral Clustering Ensemble Applied to Image Segmentation,” Front. Comput. Sci. China. 5 (1), 66–78 (2011).MathSciNetCrossRefGoogle Scholar
  10. 10.
    L. Franek and X. Jiang, “Ensemble Clustering by Means of Clustering Embedding in Vectorspaces,” Patt. Recogn. 47 (2), 833–842 (2014).CrossRefGoogle Scholar
  11. 11.
    A. Mirzaei and M. Rahmati, “A Novel Hierarchical-Clustering-Combination Scheme Based on Fuzzy-Similarity Relations,” IEEE Trans. Fuzzy Syst. 18 (1), 27–39 (2010).CrossRefGoogle Scholar
  12. 12.
    L. Zheng, T. Li, and C. Ding, “Hierarchical Ensemble Clustering,” in Proc. of 2010 IEEE Intern. Conf. on Data Mining (IEEE, 2010), pp. 1199–1204.CrossRefGoogle Scholar
  13. 13.
    E. A. Kulikova, I. A. Pestunov, and Yu. N. Sinyavskii, “Nonparametric Clustering Algorithm for Processing Large Data Arrays,” in Proc. of 14 All-Russian Conf. on Mathematical Methods of Pattern Recognition (MAKS Press, Moscow, 2009), pp. 149–152.Google Scholar
  14. 14.
    I. A. Pestunov, V. B. Berikov, and Yu. N. Sinyavskii, “Segmentation of Multispectral Images Based on an Ensemble of Nonparametric Clustering Algorithms,” Vestn. SibGAU, No. 5(31), 56–64 (2010).Google Scholar
  15. 15.
    I. A. Pestunov, V. B. Berikov, E. A.‘Kulikova, and S. A. Rylov, “Ensemble Clustering Algorithm for Large Datasets,” Avtometriya 47 (3), 49–58 (2011). [Optoelectron., Instrum. Data Process. 47 (3), 245–252 (2011)].Google Scholar
  16. 16.
    I. A. Pestunov and S. A. Rylov, “Algorithms of Spectral Texture Segmentation of Satellite Images of High Spatial Resolution,” Vestn. KemGU 2 (4(52)), 104–110 (2012).Google Scholar
  17. 17.
    M. R. Ilango and V. Mohan, “A Survey of Grid Based Clustering Algorithms,” Intern. J. Eng. Sci. Technol. 2 (8), 3441–3446 (2010).Google Scholar
  18. 18.
    L. Yonggang, W. Yi, “PHA: A Fast Potential-Based Hierarchical Agglomerative Clustering Method,” Patt. Recogn. 46 (5), 1227–1239 (2013).CrossRefGoogle Scholar
  19. 19.
    B. Leclerc, “Description Combinatoire des Ultramétriques,” Math. Sci. Humaines 127 (73), 5–37 (1981).MathSciNetGoogle Scholar
  20. 20.
    St. S. Skiena, The Algorithm Design Manual (Springer, 2008).zbMATHCrossRefGoogle Scholar
  21. 21.
    Cl. F. Olson, “Parallel Algorithms for Hierarchical Clustering,” Parallel Comput. 21 (8), 1313–1325 (1995).zbMATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    The Matlab Toolbox for Pattern Recognition, Scholar
  23. 23.
    V. Maurizio, “Principal Classifications Analysis a Method for Generating Consensus Dendrograms and its Application to Three-Way Data,” Comput. Stat. Data Anal. 27 (3), 311–331 (1998).zbMATHCrossRefGoogle Scholar
  24. 24.
    E. Achtert, H. Kriegel, E. Schubert, and A. Zimek, “Interactive Data Mining with 3D-Parallel-Coordinate-Trees,” in Proc. of ACM Intern. Conf. on Management of Data (SIGMOD), New York., 2013, pp. 1009–1012.Google Scholar
  25. 25.
    S. A. Rylov, Model of Two-Dimensional Data for Clustering, Rylov 2D Labelled 2472 elements.txt.Google Scholar

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© Allerton Press, Inc. 2015

Authors and Affiliations

  1. 1.Institute of Computational TechnologiesSiberian Branch of Russian Academy of SciencesNovosibirskRussia
  2. 2.Sobolev Institute of MathematicsSiberian Branch of Russian Academy of SciencesNovosibirskRussia

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