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Detecting clusters of specified separability for multispectral data on various hierarchical levels

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Abstract

The proposed histogram-based algorithm searches for the clustering detailedness that differs in subdomains of the vector space of spectral features depending on the average separability of clusters. The objective of the hierarchical decomposition of clusters is to achieve limit detailedness with respect to the given cluster separability. Application of the algorithm to the unsupervised classification of land cover using five-spectral satellite remote sensing data is illustrated.

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References

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Authors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics, Siberian Division, Russian Academy of Sciences, pr. Lavrentieva 6, Novosibirsk, 630090, Russia

    V. S. Sidorova

Authors
  1. V. S. Sidorova
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Correspondence to V. S. Sidorova.

Additional information

This article uses the materials of the report submitted at the 8th Open German-Russian Workshop “Pattern Recognition and Image Understanding,” on the base of Lobachevsky State university, Nizhni Novgorod, November 21–26, 2011.

Valeria S. Sidorova was born in 1947. She graduated from Novosibirsk State University (Department of Physics) in 1972. At present, she is a researcher in the Institute of Computational Mathematics and Mathematical Geophysics, Siberian Division, Russian Academy of Sciences (Novosibirsk). Her scientific interests include image processing, unsupervised classification, and texture analysis. She is the author of more than 60 publications.

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Sidorova, V.S. Detecting clusters of specified separability for multispectral data on various hierarchical levels. Pattern Recognit. Image Anal. 24, 151–155 (2014). https://doi.org/10.1134/S1054661814010155

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  • DOI: https://doi.org/10.1134/S1054661814010155

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