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Independent Component Analysis and Its Application to Classification of High-resolution Remote Sensing Images

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Subspace Methods for Pattern Recognition in Intelligent Environment

Part of the book series: Studies in Computational Intelligence ((SCI,volume 552))

Abstract

Independent component analysis (ICA) finds a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. It has been successfully applied to many problems, such as blind source separation. We apply ICA to high-resolution remote sensing images to obtain an efficient representation of color information. The three independent components are in opponent-color model by which the responses of R, G and B cones are combined in opponent fashions. This is consistent with the principle of many color systems. The interesting point is that there is no summation component that responds to the luminance channel in other transformations such as principal component analysis (PCA). The spectral independent components are then used for classification of high-resolution remote sensing images. The classification map of the independent components exhibits somewhat spatial consistency, which indicates that reduction of spectral correlation may lead to increase of spatial correlation.

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Authors and Affiliations

  1. Fort Valley State University, 1005 State University Dr., Fort Valley, GA, 31030, USA

    Xiang-Yan Zeng

  2. Ritsumeikan University, 1-1-1, NojiHigashi, Kusatus-Shi, Shiga-ken, 525-8577, Japan

    Yen-Wei Chen

Authors
  1. Xiang-Yan Zeng
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  2. Yen-Wei Chen
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Correspondence to Xiang-Yan Zeng .

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Editors and Affiliations

  1. Ritsumeikan University College of Science & Engineering, Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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Zeng, XY., Chen, YW. (2014). Independent Component Analysis and Its Application to Classification of High-resolution Remote Sensing Images. In: Chen, YW., C. Jain, L. (eds) Subspace Methods for Pattern Recognition in Intelligent Environment. Studies in Computational Intelligence, vol 552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54851-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-54851-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54850-5

  • Online ISBN: 978-3-642-54851-2

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