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ution.
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lected.
If a is a geometric shift equal to a fraction of the pixel (in
the search area)and b and c are the density gain and
shift in the target area, an equation for each pixel can be
written as:
b - g,(x) + c = g (x+a) (1)
If the image is b/w, a system of 81 equations of this type
can be written in the three unknowns a, b and c. If the
image is coloured, 81 equations for each RGB colour
have to be written. The geometrical shift a is the only
unknown used, including its r.m.s.e. The solution is
computed by using the least square method.
In numerous experimental tests it has been proved that
this sub-pixel matching gives an accuracy of positioning
that is 0.1+0.2 pixel: therefore the values of p, (and p, if
a bi-dimensional matching is carried out) can be deter-
mined with an accuracy that is similar to that of an
analytical plotter, even in the case where a poor image
resolution of 600 dpi is used.
3.2 DEM automatic extraction
A very useful feature of a digital restitution unit is the
possibility of automatic data capture for a DEM grid.
When using oriented images, this topic can be achieved
using the VLL (Vertical Line Locus) correlation algorithm.
A number n of equidistant horizontal planes, spaced at a
given AZ, are defined for each point of the regular grid
centred on an approximate value Zo: (for ex. the height of
the previous determined point). Each point P(X,Y,Z;) is
then projected by means of collinearity equations and
homologous windows are defined in each image. A cor-
relation coefficient for each pair of windows is then com-
puted. The Z value corresponding to the maximum
measure of correlation represents a more approximate
value of the unknown height.
The shape of the windows has been defined as follows:
25 x 25 pixels on both images, where different weights
are given to each pixel depending on its position.
25 15
15
25
Figure 4 - Weighted window
If the resolution is 600 dpi, the total window size is about
1 mm? on the image. This window has been subdivided
in three different frames: the weight of the pixels in the
innermost frame (5 x 5 = 25 pixels) has been fixed = 16,
in the second frame (15 x 15 - 25 = 200 pixels) the w = 2
and in the external frame (25 x 25 - 225 = 400 pixels) the
w = 1. This means that each of the three frames have an
equivalent total weight (see fig. 5).
The Z co-ordinate corresponding to the highest correla-
tion coefficient is assumed as the new approximate
height Zo». The same number n of horizontal planes, but
now spaced at AZ/2 are centred up and down the new
value of Zoo. An iterative procedure is performed until the
distance between the horizontal planes reaches a pre-
fixed value Ah. The last so found Z is assumed as the
height of the X,Y point.
If the highest correlation coefficient of the last iteration is
not acceptable (for ex. r < 0.7 for b/w images or r <
0.7x0.7x0.7=0.35 for colour images), the software asks
the operator to confirm the solution. In both cases a sub-
pixel correlation is finally made, following the procedure
described in 3.1, in order to refine the height measure-
ment.
Cs PE “max Z
: AZI2 e E Um
: RC
M AZ/4 = Ah
Zo:
MÀ. res
Figure 5 - VLL iterative process:
9 surfaces - 3 iterations
4. PRACTICAL RESULTS
4.1 Primary data calibration
A calibration grid, obtained using a contact copy on film
from a high precision glass plate, has been used for
calibrating the UMAX PS2400X scanner.
The grid of 10 x 10 square meshes, 20 mm x 20 mm
each, has been checked on an analytical plotter, in order
to define any deformations due to film shrinkage. The
values of the newly measured co-ordinates have been
used as "true" co-ordinates for calibration.
The grid has been scanned several times, in order to
check the repeatability of the scanner. The results have
shown that:
e the scanner must be “heated” for at least 1 hour
before using it for scanning. In fact, during the first
hour the geometric changes are not negligible: differ-
ences are of more than 2+3 pixels (~ 1/10 mm) along
the side of the grid (200 mm) in the X direction, i.e.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B1. Vienna 1996