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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
e There is a large amount of NO pixels in the
DB. These pixels thus influence much the global
index, generating an optimistic view.
e Even though optimistic, this global index pro-
vides a rather poor classification rate.
e [n the sub-urban area, less then half of the DB
built-up area and a little more than half of the
DB road network is seen by the expert (see
producer indexes). These values significantly
drop in the rural area, as they both reach about
15%.
e Proportionally, the road network is better de-
tected than the built-up area.
e There is a large amount of false alarms: around
30% in the sub-urban, up to more than 60% in
the rural zone
e The results are dramatically worse in the ru-
ral zone. The reason is that the image was not
well geo-referenced, so that the detected ob-
jects often fall next to the DB elements.
e The expert's performance is approximately 1596
better in both classes, while keeping a lower
false alarm rate.
A look at the confusion classes produced by the su-
perposition of the detected objects onto the DB can
give another view of the detection errors. In Fig-
ure 2, the detected elements that are superimposed
to the correct DB elements are displayed in green,
while the missing parts of the DB elements are in
red, and the detected parts that are not in the DB
(i.e. false alarms) are displayed in black. The left
and right images show parts taken from the sub-
urban and from rural area, respectively.
Eu o ue emt gan
7 Hia ra] dT P i.
ui M La EE i Pa E
Figure 2: Detail of superposing the DB onto image
DB; Green- correct detection; Black- false alarm;
Red- missing parts. Left: Rural area. Right: Sub-
urban area.
987
This method, while giving some indications con-
cerning the visibility, has several drawbacks, high-
lighted by Figures 2.
e It does not make the difference between a whole
object not detected and parts of non-detected
objects.
e [t is highly sensitive to a slight displacement
of the DB.
e Dilated points and lines have fixed sized while
small buildings have various sizes.
e Dilation of points and lines provides some tol-
erance in positioning objects but might also in-
troduce false error pixels.
Because of these drawbacks, we have designed a
matching method.
3 MATCHING METHOD
In order to avoid the confusion matrix method draw-
backs, a more sophisticated method has been im-
plemented; the confusion matrix is pixel based, while
the matching method is object-based. The DB is
divided in the road network and the built-up area,
then matching rules for each possible pair of de-
tected elements and DB object are defined and tested.
e Detected Buildings:
— Points: point — small building,
point — polygonal building
— Lines: line — small buildings,
line— polygonal building
— Polygon: polygon — small buildings,
polygon — polygonal building
e Detected Roads:
— Lines: line — linear road
— Polygon: polygon — linear road
3.1 Road network
The roads in the DB are either represented by their
circulation lanes or by their central axis, and by
their contour. The latter are stored as polygons and
the former as polylines. In the matching method,
we use the polyline representation only.