1€
te
se
House Extraction Model 1
Left Image Right Image
tr
Region Pair
House Extraction Model 2
LeftImage Right Image roof
HL
Region-Lines Pair
House Extraction Model 3
Left Image Right Image
~~
J E
~/ —
Ground
Line-Line Pairs
Fig.3. House hypothesis extraction models.
It is obvious that the possibility and reliability(P&R) of
house extraction of above three models are different.
Since the region-based process has the lowest ambiguity
in stereo matching, model 1 has the highest P&R among
above three models.
5. HOUSE CHANGE DETECTION FOR
GIS DATABASE REVISION
As mentioned in Chapter 1, house changes are limited to
two states in our current research : emergence and
demolition. It means that we will not take into account
the state of continuous change.
5.1 New-Built House Detection Model
These house hypothesis extracted from above three
models which can not find their correspondences in
existing database, are thought to be new-built houses.
Since the new-built houses have no records in existing
GIS database, the possibility and reliability(P&R) of 7
new-built house detection is basically relied on the
quality of house hypotheses extraction. Furthermore, as
the house hypotheses captured from the reign-region pairs
have the best quality and such hypotheses are generally
783
affected by the threshold values in edge and region
segmentation processes, the iterative process is generally
needed for improving the detection possibility and
reliability.
5.2 House Demolition Detection Model
For an existing house in GIS database, if there exists
corresponding house-like region or region pair and their
overlayed area is larger than a threshold T, we may say
that the house is still there. Otherwise, we say that such
a house has already disappeared. It is then easy to find the
demolition houses by comparing the house hypothesis
extracted from model 1 and model 2 with existing
database.
However, it is unreasonable to try to find all demolitions
from region pairs. Hence, 3D lines should be also an
important source for demolition detection. A 3D line
based extinction detection algorithm was then developed.
It is summarized as follows :
1) seeking all possible candidates of 3D lines relevant
to existing house A in GIS database. The possible
candidates here are these 3D lines that close enough to an
edge of a house(e.g., average distance « 3 pixels) and
having relative angle less than 25 degrees.
2) matching each edge of existing house A with
extracted 3D lines from step 1). In this matching scheme,
the height of each 3D line is the major constraint for best
match selection.
3) shifting house A with a finite tolerance and repeat
steps 1) and 2) to find the best matches.
After above iterative process finished, for the existing
house A, if there exists enough corresponding house-like
3D lines, we can say house À is still there. Otherwise,
we can get the decision that "house A has disappeared".
6. EXPERIMENTAL RESULTS AND
CONCLUSIONS
Experiments were performed to examine the accuracy of
the methods described above. As the page limitation,
only one of the testing results is demonstrated in this
paper. The original photographs used in this experiment
were taken in 1988 scaled about 1:5000. The images
utilized here are 1000X1000 pixels with an range of
intensity level from O to 255. The sequential
experimental results are illustrated in Fig.4. The results
show that the possibility and reliability of house
extraction and house change detection is affected by the
threshold values in edge extraction and about 90-95% of
the houses are extrected without interative processes
whereas the extraction rate may be increased at rate of 3-
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996