Journal of Siberian Federal University. Engineering & Technologies / Block Principal Component Analysis for Extraction of Informative Features for Classification of Hyperspectral Images

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Issue
Journal of Siberian Federal University. Engineering & Technologies. 2015 8 (6)
Authors
Pestunov, Igor A.; Melnikov, Pavel V.
Contact information
Pestunov, Igor A.:Institute of Computational Technologies of SB RAS 6 Akademika Lavrenteva, Novosibirsk, 630090, Russia; E-mail: pestunov@ict.nsc.ru,; Melnikov, Pavel V.:Institute of Computational Technologies of SB RAS 6 Akademika Lavrenteva, Novosibirsk, 630090, Russia; E-mail: pvlvlml@gmail.com
Keywords
hyperspectral image; informative feature extraction; principal component analysis; supervised classification; support vector machine
Abstract

This paper proposes a method to reduce the dimensionality of feature space for recognition of hyperspectral images. The method consists of dividing the spectral channels into blocks with high in-block correlation and the subsequent application of principal component analysis. It is shown that the proposed method allows to reduce the number of channels used in the classification by an order of magnitude with no significant degradation of recognition quality

Pages
715-725
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/19837

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

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