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Title
New perspectives to save cultural heritage
Author
Altan, M. Orhan

CIPA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
200
MDS
(pair)
Film
Agfapan APX
100
Kodak Technical Pan
all points
status = I
Ml-2
0.055
0.044
0.031
M2-3
0.060
0.043
0.036
M3-4
0.031
0.055
0.033
M4-5
0.042
0.031
0.033
M6-7
0.025
0.016
0.016
M7-8
0.025
0.017
0.015
M8-9
0.030
0.010
0.009
M9-10
0.022
0.028
0.019
M10-11
0.034
0.023
0.020
Mean
0.036
0.030
0.023
Table 1. RMSE (m) values for the individual DSM and the
average RMSE from the group for each type of film. The
right column shows the improvement after the elimination
of points with r<0.85 (See 4.3.5).
Figure 8 shows a graphic representation of the DSM
corresponding to the Ml-2 (APX 100) pair. The grey tones
respond to the depth value (z).
Figure 8. Example of DSM constructed with Ml-2 (Pan) pair.
There is an empty area (hidden) to the right of the tower.
4.3.5 Correlation coefficient value. For the detection of tie
points, it is necessary to define a threshold value that works as a
filter. In the construction of the DSM the possibility of selecting
a threshold value does not exist. The result is formed by a group
of points calculated with variable correlation values. The
application assigns a value (“status”) to each point according to
the correlation coefficient r: 1: r > 0.85; 2: 0.85 > r > 0.70, 3:
0.70 > r > 0.50. Status 4 (isolated) and 5 (suspicious) are not
defined in relation to r.
The tests of error control show that the elimination of points
with a worse correlation improves the results. Conserving the
points with a status of one exclusively improvements are
obtained in almost every case, especially in those that had
demonstrated worse precision (right column in Table 1). This
test shows that the correlation coefficient may be used as an
estimator of the data reliability.
4.4 Improvement: a synthesis DSM
It has already been indicated that the DSMs are constructed
from a sole stereoscopic pair. Since the application does not
construct a DSM by using all the data on the network, a
synthesis has been carried out which follows the steps hereafter:
• Elimination of gross errors and hidden points in each
DSM
• Elimination of all points with status * 1 (not optimum
correlation)
• Union of individual DSMs
• Error control in the synthesis DSM
The results are clear: the synthesis DSM shows a better
precision than the individual DSM. In this case, the film is a
differential factor: the DSM constructed with Agfapan APX
100 is made up of 565926 points (RMSE = 0.020 m) while with
Kodak Technical Pan 1057338 points are arrived at (RMSE =
0.015 m). That is, not only the error value improves at 25 %,
but also the quantity of points is almost double with Technical
Pan film. The improvement with respect to the group of DSM
(Table 1) is important in both cases since the RMSE is reduced
to half approximately.
4.5 Discussion
Convergent networks with multiple images still cannot be
treated with maximum results using current commercial
software. In the case of Orthobase Pro, it is possible to obtain
adjustment statistics for a converging network but it is only
possible to obtain DSM per image pair. The adjustments worsen
with the convergence angle. This fact suggests that the
converging network designs have not been used but that
adaptations have been made with algorithms of normal
geometry.
There is another evidence that indicates that the processes
accomplished with Orthobase Pro are an “inheritance” from the
standard aerial photogrammetry. For example, the Z-axis should
be orientated towards the camera in order to carry out the DTM
and it is obligatory that the façade be geo-referenced in regard
to a projection system to fulfil the orthoimage.
The improvement methods of DSM should include the detection
of blunders due to spurious correlations in hidden areas. The
criteria could be purely zone-related before the division of the
façade into visible/non-visible areas for each stereoscopic pair.
The detection of other errors may be based on the definition of
geometric constraints, at least in the façades that may be
skelotonized into simple surfaces that may serve as a geometric
reference or context.
The results improve through a selection of points based on the
correlation coefficient. The union of the individual DSMs after
an elimination of gross errors, hidden areas and poor points of
correlation notably improves the result obtaining a DSM of a
great quantity of points and a low error quantity. This may be
the path to follow in order to take advantage of the possibilities
of the software used.
The recognition of homologous points in images with different
perspectives does not seem to be efficiently resolved. A greater
effort becomes necessary in the design of efficient algorithms
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