[GRASS-user] supervised classification - feature extraction

Nikos Alexandris nikos.alexandris at felis.uni-freiburg.de
Fri May 30 14:10:32 EDT 2008

On Fri, 2008-05-30 at 13:51 -0400, M S wrote:

Dear Mark,

some ideas below. Please correct me if I am "mis-classifying" ;-)

> 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 think in any case you have to pre-define you "leftover" class.
Otherwise how else can it be "produced" if it is not on your
classification scheme?

> 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.

i.smap is very powerful. With the proper "sampling" it works great.

>   Can a class be setup that would hold all the signatures not defined
> by the training map?

Hmmm? AFAIK, any classification algorithm will use the full range of
values to split in to the number of classes that the user has selected.
So the question doesn't make sense to me.
> 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.

Although I don't have much of experience with lidar data, I think should
go (with) for them! There are no more-precise remotely sensed data than
lidar (or are there any?). Playing around with the "z" values should be
the way to do it.



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