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Band Selection for Hyperspectral Image Using Principal Components Analysis and Maxima-Minima Functional

  • Kitti Koonsanit
  • Chuleerat Jaruskulchai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6746)

Abstract

Nowadays, hyperspectral image software becomes widely used. Although hyperspectral images provide abundant information about bands, their high dimensionality also substantially increases the computational burden. An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details. In this paper, we present band selection technical using principal components analysis (PCA) and maxima-minima functional for hyperspectral image such as small multi-mission satellite (SMMS). Band selection method in our research not only serves as the first step of hyperspectral data processing that leads to a significant reduction of computational complexity, but also a invaluable research tool to identify optimal spectral for different satellite applications. In this paper, an integrated PCA and maxima-minima functional method is proposed for hyperspectral band selection. Based on tests in a SMMS hyperspectral image, this new method achieves good result in terms of robust clustering.

Keywords

Band Selection Principal Components Analysis PCA Satellite image Maxima-Minima Functional 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kitti Koonsanit
    • 1
  • Chuleerat Jaruskulchai
    • 1
  1. 1.Department of Computer Science, Faculty of ScienceKasetsart UniversityBangkokThailand

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