Some lidar vendors make by default ground etc. classification. For example Swedish NLS provides ground and water. Finnish NLS provides ground and some vegetation. Rest is unclassified. My assumption is that they classify automatically such things they can trust for sure and leave rest empty.
Erdas Imagine Pointcloud classifier makes always a full classification from scratch. So everything is always reclassified. I am not sure can we produce better result for those that someone else has already made. So are we really improving or decreasing result a bit?
I have a decent workaround by using spatial modeller and select only unclassified points to classification. That works pretty OK but fails as not all points are included in process. So I can do the thing I want but only with subset of points which decreases the output quality. What I would like to do is full classification using all points in dataset but new values written just points that had no classification before.
So simple tick in lidar point cloud classifier - classify only unclassified points. That is my request in compressed form.
As an attcahment simple sample model how to work it out but as told it has its weak points too - mainly for decresing the data used and by that compromizing the output quality a bit