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Hierarchical cluster algorithm for remote sensing data of earth

  • Representation, Processing, Analysis, and Understanding of Images
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Abstract

A new automatic, hierarchical, multidimensional, histogram-based clusterization algorithm is considered. A method for choosing the clusterization detailedness in different regions of the vector space of spectral features depending on the average separability of clusters is proposed. The algorithm is applied for the automatic classification of multispectral satellite data in recognizing the land cover.

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Authors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of the Russian Academy of Sciences, pr. Lavrent’eva 6, Novosibirsk, 630090, Russia

    V. S. Sidorova

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

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Valeriya Sergeevna Sidorova. Born 1947. Graduated from the Department of Physics of Novosibirsk State University in 1972. Scientific interests: image processing, unsupervised classification, and analysis of textures. Author of more than 50 papers. Research scientist at the Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of the Russian Academy of Sciences (Novosibirsk).

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Sidorova, V.S. Hierarchical cluster algorithm for remote sensing data of earth. Pattern Recognit. Image Anal. 22, 373–379 (2012). https://doi.org/10.1134/S1054661812020149

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  • DOI: https://doi.org/10.1134/S1054661812020149

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