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Hierarchical clustering algorithms for segmentation of multispectral images

  • Analysis and Synthesis of Signals and Images
  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

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.

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Authors and Affiliations

  1. Institute of Computational Technologies, Siberian Branch of Russian Academy of Sciences, pr. Lavrent’eva 6, Novosibirsk, 630090, Russia

    I. A. Pestunov & S. A. Rylov

  2. Sobolev Institute of Mathematics, Siberian Branch of Russian Academy of Sciences, pr. Akademika Koptyuga 4, Novosibirsk, 630090, Russia

    V. B. Berikov

Authors
  1. I. A. Pestunov
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  2. S. A. Rylov
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  3. V. B. Berikov
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Corresponding author

Correspondence to I. A. Pestunov.

Additional information

Original Russian Text © I.A. Pestunov, S.A. Rylov, and V.B. Berikov, 2015, published in Avtometriya, 2015, Vol. 51, No. 4, pp. 12–22.

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Pestunov, I.A., Rylov, S.A. & Berikov, V.B. Hierarchical clustering algorithms for segmentation of multispectral images. Optoelectron.Instrument.Proc. 51, 329–338 (2015). https://doi.org/10.3103/S8756699015040020

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  • DOI: https://doi.org/10.3103/S8756699015040020

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