[GRASS-user] supervised classification - feature extraction

[M S]

I realize one can use use a training map to cluster like features, but is
there a way to have a "leftover" class that throws everything else that
doesnt match a defined class into this "leftover" category?

I'm using i.gensig to generate a signature file from a training map, and
then i.maxlik to classify the raster.  It works very well, but I am seeming
to find that by defining 7 classes, it wants to put something in every
class, even if not a best match.  It is quite possible I am missing
something critical, or not using the proper module.  It looks like I should
give i.smap a try too.  Can a class be setup that would hold all the
signatures not defined by the training map?

Also, in trying to extract houses, because of the wide variety of colors of
roof shingles, it does not seem to work very well because of all the
variance with in that class.  I have lidar data for this area, and was
thinking that for houses/buildings it might be a better approach to use
lidar processing tools.

Basically I'm looking to classify impervious features within a given
watershed.  there was a good link here about this (
http://www.perrygeo.net/wordpress/?p=104 ), but the watershed I'm working
with has huge areas of residential development.


Mark
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