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Multispectral Transformations of Image Data

  • John A. Richards
  • Xiuping Jia

Abstract

The multispectral or vector character of most remote sensing image data renders it amenable to spectral transformations that generate new sets of image components or bands. These components then represent an alternative description of the data, in which the new components of a pixel vector are related to its old brightness values in the original set of spectral bands via a linear operation. The transformed image may make evident features not discernable in the original data or alternatively it might be possible to preserve the essential information content of the image (for a given application) with a reduced number of the transformed dimensions. The last point has significance for displaying data in the three dimensions available on a colour monitor or in colour hardcopy, and for transmission and storage of data.

Keywords

Covariance Matrix Pixel Point Pixel Vector Origin Shift Dimensional Histogram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References for Chapter 6

  1. N. Ahmed and K.R. Rao, 1975: Orthogonal Transforms for Digital Signal Processing, Berlin, Springer-Verlag.MATHCrossRefGoogle Scholar
  2. H.C. Andrews, 1972: Introduction to Mathematical Techniques in Pattern Recognition, New York, Wiley.MATHGoogle Scholar
  3. E.F. Byrne, P.F. Crapper and K.K. Mayo, 1980: Monitoring Land-Cover Change by Principal Components Analysis of Multitemporal Landsat Data. Remote Sensing of Environment, 10, 175–184.CrossRefGoogle Scholar
  4. N.A. Campbell, 1996: The Decorrelation Stretch Transformation. Int. J. Remote Sensing, 17, 1939–1949.CrossRefGoogle Scholar
  5. E. P. Crist and R. T. Kauth, 1986: The Tasseled Cap De-Mystified. Photogrammetric Engineering and Remote Sensing, 52, 81–86.Google Scholar
  6. R.C. Gonzalez and R.E. Woods, 1992: Digital Image Processing, Mass., Addison-Wesley.Google Scholar
  7. P.J. Howarth and E. Boasson, 1983: Landsat Digital Enhancements for Change Detection in Urban Environments. Remote Sensing of Environment, 13, 149–160.CrossRefGoogle Scholar
  8. S.E. Ingebritsen and R.JP. Lyon, 1985: Principal Components Analysis of Multitemporal Image Pairs. Int. I Remote Sensing, 6, 687–696.CrossRefGoogle Scholar
  9. S.K. Jensen and F.A. Waltz, 1979: Principal Components Analysis and Canonical Analysis in Remote Sensing. Proc. American Photogrammetric Soc. 45th Ann. Meeting, 337-348.Google Scholar
  10. R. J. Kauth and G.S. Thomas, 1976: The Tasseled Cap — A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. Proc. LARS 1976 Symp. on Machine Process. Remotely Sensed Data, Purdue University.Google Scholar
  11. J. A. Richards, 1984: Thematic Mapping from Multitemporal Image Data Using the Principal Components Transformation. Remote Sensing of Environment, 16, 35–46.MathSciNetCrossRefGoogle Scholar
  12. A. Santisteban and L. Muñoz, 1978: Principal Components of a Multispectral Image: Application to a Geologic Problem. IBM J. Research and Development, 22, 444–454.CrossRefGoogle Scholar
  13. J.M. Soha and A.A. Schwartz, 1978: Multispectral Histogram Normalization Contrast Enhancement. Proc. 5th Canadian Symp. on Remote Sensing, 86-93.Google Scholar
  14. P.H. Swain and S.M. Davis (Eds), 1978: Remote Sensing: The Quantitative Approach, New York, McGraw-Hill.Google Scholar
  15. M. M. Taylor, 1973: Principal Components Colour Display of ERTS Imagery. Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351, 1877-1897.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • John A. Richards
    • 1
  • Xiuping Jia
    • 2
  1. 1.Research School of Information Sciences and EngineeringThe Australian National UniversityCanberraAustralia
  2. 2.School of Electrical Engineering University CollegeThe University of New South Wales, Australian Defence Force AcademyCampbellAustralia

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