AI-GEOSTATS: Responses to: Maximum Autocorrelated Factors
- Dear List,
Here are the responses I have received to date concerning the matter above.
Thanks to all
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.
Mark G Sweeney
Rio Tinto Technical Services (TS)
1 Research Avenue
Phone: (+61) 3 9242 3278
Mobile: 0407 357 877
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
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
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@...]
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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