Hello, Factorial kriging is not very sophisticated, it's just a slight variant of kriging that requires the modification of just a few lines of codes. Anyways, I just posted a program to perform factorial kriging analysis in the download section of my website. I hope your grid is not too big,
The program filter.exe (FORTRAN source code filter.f) is a modified version of the Gslib program kt3d.f that allows performing a kriging analysis. Based on the number of nested structures of the variogram model specified in the parameter file filter.par, the program will estimate the values of the noise and noise-filtered signal (1 structure), or the values of the noise, local and regional components (2 structures). Following Goovaerts (1997, page 167), the regional component includes both the long-range component and the trend component in order to attenuate the impact of the search window on the estimation of these long-range spatial components. The zipped folder includes the executable, the source code, as well as a sample parameter file for the Jura dataset. In another paper concerned with noise-filtering of imagery, I run the program for a single pixel to get the kernel weights and then apply the same kernel everywhere (since the data geometry does not change except at the edges of the image). Goovaerts, P., Jacquez, G.M., and W.A. Marcus. 2005. Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of anomalies. *Remote Sensing of the Environment*, 95, 351-367.<http://home.comcast.net/%7Epgoovaerts/RSE-2005.pdf> Cheers, Pierre On Tue, Feb 2, 2010 at 3:39 AM, seba <sebastiano.trevis...@libero.it> wrote: > Hi Pierre > > I think that for my task factorial kriging is a little bit > too much sophisticated (nevertheless, is there any open source or > free implementation of it ??? I remember that it is implemented in > Isatis.....). > > I have an exhaustive and regularly spaced data set (i.e. a grid) and I need > to calculate locally the spatial variability of the residual surface or > better > I would like to calculate the spatial variability of the high frequency > component. > Here I'm lucky because I know exactly what I want to see and what I need to > filter out. > In theory, using (overlapping) moving window averages (but here it seems > better to use some more complex kernel) > one should be able to filter out the short range variability (characterized > by an eventual variogram range within the window size???). > Seeing the problem from another perspective, in the case of a perfect > sine wave behavior, I should be able to filter out spatial > variability components with wave lengths up to the window size. > But maybe there is something flawed in my reasoning....so feedback is > appreciated! > Bye > Sebastiano > > > > > > At 16.27 01/02/2010, you wrote: > > well Factorial Kriging Analysis allows you to tailor the filtering weights > to the spatial patterns in your data. You can use the same filter size but > different kriging weights depending on whether you want to estimate > the local or regional scales of variability. > > Pierre > > 2010/2/1 seba < sebastiano.trevis...@libero.it> > Hi José > Thank you for the interesting references. I'm going to give a look! > Bye > Sebastiano > > > > At 15.46 01/02/2010, José M. Blanco Moreno wrote: > > Hello again, > I am not a mathematician, so I never worried too much on the theoretical > reasons. You may be able to find some discussion on this subject in Eubank, > R.L. 1999. Nonparametric Regression and Spline Smoothing, 2a ed. M. Dekker, > New York. > You may be also interested on searching information in and related to > (perhaps citing) this work: Altman, N. 1990. Kernel smoothing of data with > correlated errors. Journal of the American Statistical Association, 85: > 749-759. > > En/na seba ha escrit: > > Hi José > Thank you for your reply. > Effectively I'm trying to figure out the theoretical reasons for their use. > Bye > Sebas > > > > > > -- > Pierre Goovaerts > > Chief Scientist at BioMedware Inc. > 3526 W Liberty, Suite 100 > Ann Arbor, MI 48103 > Voice: (734) 913-1098 (ext. 202) > Fax: (734) 913-2201 > > Courtesy Associate Professor, University of Florida > Associate Editor, Mathematical Geosciences > Geostatistician, Computer Sciences Corporation > President, PGeostat LLC > 710 Ridgemont Lane > Ann Arbor, MI 48103 > Voice: (734) 668-9900 > Fax: (734) 668-7788 > > http://goovaerts.pierre.googlepages.com/ > > -- Pierre Goovaerts Chief Scientist at BioMedware Inc. 3526 W Liberty, Suite 100 Ann Arbor, MI 48103 Voice: (734) 913-1098 (ext. 202) Fax: (734) 913-2201 Courtesy Associate Professor, University of Florida Associate Editor, Mathematical Geosciences Geostatistician, Computer Sciences Corporation President, PGeostat LLC 710 Ridgemont Lane Ann Arbor, MI 48103 Voice: (734) 668-9900 Fax: (734) 668-7788 http://goovaerts.pierre.googlepages.com/