[GRASS-user] supervised classification - feature extraction

[M S]

That would be great.   Let's say for example that I only want to classify
paved or dirt roads.  So I setup two classes, one training area for dirt
road, and one for paved road.  Then a third outlier class of everything else
that doesnt match the two inputted training classes.

It almost seems like using an unsupervised classification could achieve
this, and then only extract the features of interest, being types of roads
or impervious features.

I have lidar intensity data, but it is single band, and I am presuming that
this 1 foot pixel multiband true color is better input for defining unique
signatures.


Mark

On Fri, May 30, 2008 at 10:49 PM, Hamish <hamish_b at yahoo.com> wrote:

> Mark:
> > 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?
>
> ie you want an "outliers" class?
>
>
> Hamish
>
>
>
>
>
>
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