ighbouring,
vn in figure
tions.
| mixes the
eft), due to
vegetation.
considering
/ low. The
trongly im-
5.
hm reaches
esidual val-
ility in the
m inappro-
the image
1S residuals
in the RMS
1 about 2%
ignificantly
Significant
for 1:5,000
information
ctures.
systematic
iges of the
igated, but
be verified
the digital
for exterior
workstation
1tersections
any kind of
: automatic
RMS differences RMS residuals
Type/level Angles XYZ Image points | Control points H, V | Blunders
[gon] [m] [um] [m] [^6]
Reference 0.0000 | 0.000 2.7 0.046, 0.086 0.0
Approx./starting val. | 2.6569 | 28.520 — — v
Level 5, 480 um 0.4027 | 4.881 83.4 0.035, 0.030 15.0
Level 4, 240 jm 0.1151 | 1.344 35.6 0.132, 0.170 2.1
Level 3, 120 um 0.0477 | 0.639 39.4 0.088, 0.141 1.9
Level 2, 60 um 0.0475 | 0.620 6.6 0.107, 0.144 10.0
Level 1, 30 um 0.0263 | 0.355 4.1 0.148, 0.187 2.0
Level 0, 15 um 0.0309 | 0.379 3.3 0.127, 0.106 2
Table 2: Results for the image pyramid iterations. 'RMS differences' denote RMS differences between reference orientation
parameters and automatically computed parameters in each image level. 'RMS residuals' denote RMS residuals in image arid
object space, and is a result of inaccuracies in the digital map and the automatic image measurements.
orientation and subsequent computation of orthophotos.
7 CONCLUSION
If the results in this test can be verified by further testing,
the method presented in this paper may be profitably applied
to map revision, orthophoto production, and thematic map-
ping. Even better results may be reached by extending the
matching algorithm to smaller object types, e.g. manhole
covers and gratings. The potential benefit is to avoid signal-
ization, surveying, aerotriangulation and any extra equipment
like GPS/INS.
The proposed method for exterior orientation has been ap-
plied to 1:5,000 photographs and large scale digital topo-
graphical/technical T3 maps. It seems evident that T3 maps
are sufficiently accurate for this purpose.
Problems may occur in case of many similar looking road in-
tersections. In densely built city areas, occlusions and shad-
ows may give rise to many blunders. Pavements, of which
there are very few in the test area, has no specific code in the
map data, and may be found difficult or impossible to handle.
For rural areas, the presented approach should be applied to
1:25,000 photographs and TO/TK1 map data.
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