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AI-GEOSTATS: Responses to: Maximum Autocorrelated Factors

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  • Masters, Stuart (TS)
    Dear List, Here are the responses I have received to date concerning the matter above. Thanks to all Stuart ==================================================
    Message 1 of 1 , Apr 17, 2002
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      Dear List,

      Here are the responses I have received to date concerning the matter above.

      Thanks to all



      Hi Stuart

      I believe that the PCA methodology is most commonly used for multivariate data, due to its simplicity. You might want to refer to:

      "Multivariate Geostatistics" by Hans Wackernagal, published by Springer.

      A linear transformation is defined which transforms a set of correlated variables into uncorrelated factors. These orthogonal factors can then be shown to extract successively a maximal part of the total variance of the variables.

      God luck,


      Mark G Sweeney

      Rio Tinto Technical Services (TS)
      Principal Consultant
      1 Research Avenue
      Bundoora, 3083

      Phone: (+61) 3 9242 3278
      Mobile: 0407 357 877
      mailto: mark.sweeney@...


      Dear Stuart.

      I good reference paper is:

      Geostatistical Simulations of regio0nalized Pore-Size Distributions Using
      Min?Max Autocorrelation Factors.
      A. J. Desbarats and R. Dimitrakopoulos
      Mathematical Geology Vol. 32, N 8 2000
      p 919-942


      Márcio Bastos Fonseca


      There was a tech report from Stanford Univ. Stat Dept about 1984 by Paul Switzer and Green, I think it subsequently appeared as a paper in the Canadian J. Statistics but 8-10 yrs later. I think Paul is still at Stanford so you could contact him there. They were looking at multispectral data, Landsat I think.

      Somthing that might be close as well
      1995, Tailiang Xie and Myers, D.E., Fitting Matrix valued variogram models by simultaneous diagonalization: I Theory. Math. Geology 27, 867-876 1995,Tailiang Xie, Myers, D.E. and Long, A.E., Fitting Matrix valued variogram models by simultaneous diagonalization: II Applications. Math. Geology 27, 877-888
      Donald E. Myers


      Is the following reference useful ?


      PS: Sorry, I don't have the paper but could find the following abstact in a


      Journal: Remote Sensing of Environment

      Volume 64, Issue 1
      April 1998
      Pages 1-19


      Multivariate Alteration Detection (MAD) and MAF Postprocessing in
      Multispectral, Bitemporal Image Data: New Approaches to Change Detection


      Allan A. Nielsena, Knut Conradsena and James J. Simpsonb


      This article introduces the multivariate alteration detection (MAD)
      transformation which is based on the
      established canonical correlations analysis. It also proposes using
      postprocessing of the change detected by the
      MAD variates using maximum autocorrelation factor (MAF) analysis. The MAD and
      the combined MAF/MAD
      transformations are invariant to linear scaling. Therefore, they are
      insensitive, for example, to differences in gain
      settings in a measuring device, or to linear radiometric and atmospheric
      correction schemes. Other multivariate
      change detection schemes described are principal component type analyses of
      simple difference images. Case
      studies with AHVRR and Landsat MSS data using simple linear stretching and
      masking of the change images
      show the usefulness of the new MAD and MAF/MAD change detection schemes.
      Ground truth observations
      confirm the detected changes. A simple simulation of a no-change situation
      shows the accuracy of the MAD and
      MAF/MAD transformations compared to principal components based methods.

      Gregoire Dubois [gregoire.dubois@...]


      Stuart Masters
      Principal Consultant - Geology & Geostatistics
      Rio Tinto Technical Services
      Level 25, Central Park
      152 -158 St. Georges Terrace
      Perth, WA, Australia 6000

      Ph: + 61 8 9327 2984
      FAX: + 61 8 9327 2999
      Mob: 0438 394 380

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