isprsannals II 3 W3 1 2013
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In this paper two different ways to refine the selection of façade planes are considered: (i) the point cloud is co-registered to an existing city model, which is used
We extract the regions that might belong to road segments from the class others and utilize that information to initialize a final graph optimization performed with five
The achieved results show the off-ground region (Figure 5b), the roof region exploiting the spectral information (Figure 5c) and the final classification (Figure
To restrict the search range for image patch based classification from all possible pixel positions (e.g. 21MPix) to a small number of probable candidates (50,000), we first apply
The authors thank the IGN (Institut National de l’Information Géographique et Forestière, France) for providing reliable ground truth data. Adaptive Filtering of
Our system works with more than 140 different classes of traffic signs and does not require labor-intensive labelling of a large amount of training data due to the
Then the different data sets were georeferenced in the same reference system using 6 points accurately collected on high resolution scan and used as GCP (Ground Control Points)
Figure 2: Separating nearby buildings: (a) input (non-ground) point cloud data, (b) extracted building footprint (buildings are merged), (c) corresponding building mask, (d)
