Journal of Siberian Federal University. Engineering & Technologies / Histogram Hierarchical Algorithm and the Reduction of the Dimensionality of the Spectral Features Space

Full text (.pdf)
Issue
Journal of Siberian Federal University. Engineering & Technologies. 2017 10 (6)
Authors
Sidorova, Valerija S.
Contact information
Sidorova, Valerija S.: Institute of Computational Mathematics and Mathematical Geophysics SB RAS 6 Akademika Lavrentieva, Novosibirsk, 630090, Russia;
Keywords
remote sensing; clustering; multidimensional histogram; cluster rasilimali; own vectors space
Abstract

This paper proposes the algorithm for dimension reduction of data in the process of hierarchical histogram clustering data of remote sensing of the Earth. Application of the algorithm is illustrated to multispectral data. Clustering large amount of data remote sensing is usually carried out in two ways: by K medium (in advance, you must know the number of clusters K and an approximation of the data distribution), and histogram. Here we propose a hierarchical histogram algorithm, which does not require to specify the number of clusters and is quick. This paper considers the issue of reducing the dimension of own space of features, obtained by hierarchical histogram algorithm. Getting clusters of multispectral image, pay attention to the fact that the different clusters corresponding to different objects on Earth can be characterized by different dimensionality of the data, i.e., the set of spectral channels coming from the satellite, it may be unnecessary for a number of objects. Also, the level of detail of clustering can be different in different clusters

Pages
714-722
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/35004

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