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.<br>
<br>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.<br><br>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. <br>
<br><br>Mark<br><br><div class="gmail_quote">On Fri, May 30, 2008 at 10:49 PM, Hamish <<a href="mailto:hamish_b@yahoo.com">hamish_b@yahoo.com</a>> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Mark:<br>
<div class="Ih2E3d">> I realize one can use use a training map to cluster like features,<br>
> but is there a way to have a "leftover" class that throws everything<br>
> else that doesnt match a defined class into this "leftover" category?<br>
<br>
</div>ie you want an "outliers" class?<br>
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