[GRASS-user] supervised classification - feature extraction
dylan.beaudette at gmail.com
Sat May 31 11:41:48 EDT 2008
On Sat, May 31, 2008 at 6:23 AM, M S <mseibel at gmail.com> wrote:
> 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
I have used the i.smap classifier on BW imagery with good results. I
was after open and closed canopy, but included a third "shadow" class
for the small shadows cast by the taller trees to pick up the
"outlier" pixels. If you can delineate some of your "outlier" regions
in your training map, it may be possible to discriminate between your
real classes and the new outlier class.
> On Fri, May 30, 2008 at 10:49 PM, Hamish <hamish_b at yahoo.com> wrote:
>> > 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?
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