Semantic Interpretation of Digital Aerial Images Utilizing ...

20 Chapter 2. Digital Aerial Imagery
we therefore discuss how to derive the terrain model from the DSM.
2.4 Digital Terrain Model
The dense point cloud, provided by the dense matching process, can be seen as an uninterpreted
representation of visible surfaces of observed scenes. In the literature, there exists a
variety of techniques to construct a terrain model. These techniques range from applying
local operators [Eckstein and Munkelt, 1995, Weidner and Förstner, 1995], over utilizing
surface models [Champion and Boldo, 2006,Sohn and Dowman, 2002] and different segmentation
[Sithole and Vosselman, 2005] or even recognition approaches [Zebedin et al.,
2006], to using hybrid methods [Baillard, 2008].
In this thesis we used a local filtering strategy and a variational strategy to separate
the available 3D points into those that describe the terrain surface and those that represent
elevated objects. By taking into account the derived range images from overlapping views
(we use an approximated median to efficiently integrate the height observations from
four neighboring views) and the available camera data, a filtering strategy detects local
minimums (defining points on ground) over different scales in the fused point cloud. A
derived probability map encodes sampled image regions, that have been formerly elevated
by buildings and trees. Similar to the approach described in [Unger et al., 2010], we make
use of a variational in-painting strategy in order to fill these areas with meaningful terrain
height values.
Subtracting the DTM from the DSM delivers the absolute elevation measurements of
the objects, which can now be used as a discriminative feature cue for the proposed semantic
interpretation workflow (see Chapter 3) or for the estimation of real object heights,
required for the construction of 3D models. Figure 2.7 depicts derived elevation measurements
for an image of Dallas. Note that these elevation maps provide absolute distance
measurements, that can be easily used to distinguish between objects located on the
ground and elevated object, like trees and buildings.
2.5 Orthographic Image Representation
In aerial imaging, orthographic projections are a often used to represent the mapped information.
Web-driven initiatives, like Google Earth and Microsoft Bing Maps, frequently
offer imagery in the form of 2D ortho-photos due to efficiency. These orthographic images
are commonly derived from perspective imagery by transforming the input sources
to a parallel projection, where each sampled has then a constant pixel size. Due to the
projection, vertical objects, like building facades, are represented as 2D lines in the re-