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Christoph Kaeser
5.2 Roads
More details on this project part are given in Zhang and Baltsavias (2000). In a first step of road reconstruction, we aim
at detecting existing roads, while roads that do not exist anymore or new ones will be treated later. We first concentrate
on roads, ignoring other transportation network objects like railway lines, mountain paths etc. In contrast to other
approaches for road extraction, the proposed approach uses multiple cues about the object existence, employs existing
CO rules and models, and treats each road subclass differently to increase success rate and reliability of the
results.
The initial knowledge database is established by the information extracted from existing geographic data and road
design rules. This offers a geometric, topological and contextual description of road network in the scene. The database
is automatically updated and refined using information gained from image analysis. Colour cues, expressed in the form
of colour region attributes, are also used to support stereo matching and improve the performance of 2D and 3D
grouping when combined with geometric cues. Since neither 2D nor 3D procedures alone are sufficient to solve the
problem of road extraction, we propose to extract the road network with the mutual interaction of 2D and 3D
procedures. Hence, the main steps of road extraction are: building-up of the knowledge base for each road segment in
VECTOR25, finding 3D straight lines in a search region defined by the VECTOR25 data, classification of image
patches, extraction of other cues, combination of various cues guided by the knowledge database to find plausible
groups of road edges for each VECTOR25 road segment and refinement and update of the knowledge database. The
general strategy is shown in Fig. 5. Fig. 6 shows more details of the results of image processing and derivation of
subclass vector attributes.
: +
Database for road Image processing
network + ;
Input data from L+T m» : ; ;
- Class : | Partial results :
- Road type ; :
- Road marks be C emis )
- Geometry with : :
3D info : Knowledge :
- Width : Base
- Length
Road design rules, - . Horizontal & : :
other knowledge ——» vertical ; Cue :
curvature : combination :
= eTopolosy info ome
- Landcover [
4 - Results
- Accuracy estimation |<
Figure 5. Strategy of road network extraction in ATOMI.
zu
Feature extraction ; ;
Stereo colour D] Image matching > 3D straight lines
aerial images :
- 2D road regions
mm - Shadows
Li, image analysis —» cad marks
Cats, ...
Pg We :
VECTOR25 and | > Subclass attribute |g] - Road attributes
other input data derivation - Landcover
- Slope
Figure 6. Details of image processing and derivation of subclass vector attributes.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 467
