CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation
the highest frequency of the signal.”). For our situation it means
that sensor’s geometric resolution determines the object
resolution, or in other words, the level of detail of the object.
Table2: Quantitative results.
scale
A
B
B
C
C
type
urban
low
urban
low
urban
low urban
low
urban
sensors
airborne
airborne
space
bome
space
bome
space
bome
method
Seeding
Seeding
Seeding
Matching
Texture
Analysis
CFB
80.5%
90.2%
97%
88.3%
34.4%
NFB
19.5%
9.8%
3%
11.7%
65.6%
WFB
7.3%
4.8%
30.3%
1.7%
17.2%
3.2 Qualitative Assessment of Building Extraction
The qualitative analysis is based on a comparison between the
building outlines derived by using the proposed automated
methodology, and manually mapped buildings (image
restitution). The manual mapping is carried out by a
professional operator who performs 2D (or 3D) digitization on
the input data (oriented imagery or orthophotos).
The residuals of each building comer from the manual mapping
and the closest point of the automatically extracted shape are
computed as a quality measure.
The results are categorized in three groups depending on the
method used for building extraction (Table 3). The RMS is
given in pixels.
Table3: Qualitative results.
Hough
Matching
Texture
Analysis
RMS x
0.937
0.898
0.954
RMS y
0.914
0.958
0.996
total RMS
1.309
1.313
1.379
The number of examined objects is the same as in the
quantitative analysis.
Note that the figures in Table 3 are based on image residuals.
They show the difference of the automatically derived comer
points and the digitized ones in the image. As our data sets were
acquired with vertical viewing angles these results can be also
interpreted as planimetric object space residuals.
But when dealing with images that were captured with oblique
viewing angles, the buildings must be projected into object
space in order to carry out a qualitative analysis in the reference
system.
4. CONCLUSIONS
The aim of this work was to propose a method for generating
DCMs which makes use of images from spacebome or airborne
line scanning devices, on orthophotos if available and on
elevation models. Various image processing techniques, such as
Hough transformation, adaptive region growing, image
matching, texture analysis, were employed and investigated for
deriving the strengths and weaknesses of each. A variety of data
sets were tested, coming from both spacebome and airborne
acquisition systems. Through the research based on adaptive
region growing and on the iterative Hough transformation we
can conclude that the method is very powerful, but has also
some weaknesses. One is the high dependence on the
radiometric quality of the input imagery. Furthermore, rather
small buildings will not be treated correctly. Image matching
proved to be a very effective, but very time consuming. The
suggested strategy of texture analysis, although very efficient
for pattern recognition over areas in small scale imagery, was
not very successful for extracting individual buildings.
Through this research partly very good results were obtained,
but nevertheless further investigations are necessary for
improving the quality of the results even more.
Future work will be focused on:
• Extraction of objects with holes (e.g. houses with
inner courtyards), i.e. deriving the inner and outer
boundary of buildings.
• Research on constraint settings for aggregating
neighbouring roof parts that belong to one building.
• Introduction of multispectral information for making
the algorithms more efficient, especially as far as seed
point determination is concerned.
• Extract edges on sub-pixel bases.
• Integrate a hierarchical approach in order to decrease
computation time.
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