[GRASS-dev] grass classification routines and small / large numbers

[Dylan Beaudette]

Hi,

I have found that using the GRASS classification modules work well when the 
inputs come from discreet (0-255) distributions- for example landsat 
channels, etc. - however I seem to get a lot of singularity problems, or maps 
with a single class when using floating point values of different magnitude. 
I am imagining that this is due to scaling issues - and perhaps badly 
behaving algorithms when input variables are both very small and very large. 
I have found that when using clustering approaches in R, it is possible to 
pre-standardize the input data, which usually results in much more 
interpretable results. Are there any particular gotchas associated with the 
GRASS modules which one should be wary of ?

I am mainly asking to avoid the memory limitations of R- loading 6-8 large 
grids usually fills the available memory.

Cheers,


-- 
Dylan Beaudette
Soils and Biogeochemistry Graduate Group
University of California at Davis
530.754.7341