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 _______________________________________________ grassuser mailing list [email protected] http://grass.itc.it/mailman/listinfo/grassuser
