Chapter 6. Hyperspectral Image Data Conditioning and Regression Analysis

  1. Hans F. Grahn2 and
  2. Paul Geladi3
  1. James E. Burger1 and
  2. Paul L. M. Geladi3

Published Online: 19 NOV 2007

DOI: 10.1002/9780470010884.ch6

Techniques and Applications of Hyperspectral Image Analysis

Techniques and Applications of Hyperspectral Image Analysis

How to Cite

Burger, J. E. and Geladi, P. L. M. (2007) Hyperspectral Image Data Conditioning and Regression Analysis, in Techniques and Applications of Hyperspectral Image Analysis (eds H. F. Grahn and P. Geladi), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470010884.ch6

Editor Information

  1. 2

    Division of Behavioral Neuroscience, Department of Neuroscience, Karolinska Institutet, S-17177, Stockholm, Sweden

  2. 3

    NIRCE, The Unit of Biomass Technology and Chemistry SLU Röbäcksdalen, PO Box 4097, SE 90403 Umeå, Sweden

Author Information

  1. 1

    Burger Metrics, Applied Hyperspectral Imaging for Automation and Research, Ladehammerveien 36, 7041 Trondheim, Norway

  2. 3

    NIRCE, The Unit of Biomass Technology and Chemistry SLU Röbäcksdalen, PO Box 4097, SE 90403 Umeå, Sweden

Publication History

  1. Published Online: 19 NOV 2007
  2. Published Print: 27 SEP 2007

ISBN Information

Print ISBN: 9780470010860

Online ISBN: 9780470010884

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Keywords:

  • hyperspectral image data;
  • multiplicative scatter correction (MSC);
  • multivariate image regression;
  • root mean square error of prediction (RMSEP);
  • Instrument signal transformation;
  • standard reference materials (SRMs);
  • piecewise multiplicative scatter correction (PMSC);
  • extended multiplicative signal correction (EMSC);
  • standard normal variate (SNV) transform

Summary

This chapter contains sections titled:

  • Introduction

  • Terminology

  • Multivariate Image Regression

  • Data Conditioning

  • PLS Regression Optimization

  • Regression Examples

  • Conclusions

  • Acknowledgements

  • References