# Full text: New perspectives to save cultural heritage

```CIPA 2003 XIX th International Symposium, 30 September-04 October, 2003, Antalya, Turkey
range (depending on the nominal scale of the map base it is
intended to be adopted). The MIVIS image geocoding is
therefore a delicate step to pass through; further complexities
come from the whiskbroom MIVIS sensor model which
introduces many deformations it’s important to take care of.
Scene geometry has to be corrected. Usual methodology based
on simple polynomial approach cannot model such geometry
especially in a mountain region as the study one is.
Orthoprojection has to be done to make MIVIS data suitable for
the subsequent data integration.
The whole process path falls into four main steps:
a) Image orthoprojection;
b) Significant band selection;
c) Test area selection and image masking;
d) Image classification and validation.
Figure 1. Area acquired by MIVIS airborne scanner.
3. IMAGE ORTHOPROJECTION
The MIVIS sensor is a whiskbroom one. A rigorous sensor
model is not easy to describe and ancillary data needed for such
operation (attitude and position recordings) are often not
available. In the rigorous model each pixel is acquired with its
own perspective different from the other ones. Rigorous
approach based upon collinearity equations is not appropriate in
this case. Collinearity equations would have to be modified to
take the dependence of the sensor attitude and position on the
time into consideration. Such a complex model can successfully
be substituted by general projecting algorithms. The Rational
Function Model (RFM) is one of the most commonly used non-
parametric one. This model relates the image coordinates (>,0)
and the three dimensional terrain coordinates (X, Y,Z) through a
ratio of 3 rd order (maximum) polynomials Pi (20 coefficients), as
shown below;
e P a (X,Y,Z)
P„(X,Y,Z)
p c (x,Y,z)
P d (X,Y,Z)
These equations are called RFM upward equations.
The model has been implemented in IDL programming
language in order to control the behavior of this methodology.
It has been found that it is closely related to the geometric
distortion due to the sensor attitude, to the elevation range of the
scene and above all to the number and distribution of the used
Ground Control Points (GCPs). In this particular case image
orthoprojection was carried out using the complete RFM (3 rd
polynomial). 57 GCPs were collected to estimate the model
coefficients. The residuals on the GCPs are presented below.
ID
Residual
(pixels)
Res x
(pixels)
Res y
(pixels)
ID
Residual
(pixels)
Res x
(pixels)
Res y
(pixels)
1
Check Point
32
1,84
1,84
-0,01
2
3,01
-2,74
1,24
33
0,74
0,10
-0,74
3
2,88
0,87
-2,74
34
2,32
-1,14
-2,02
4
2,52
0,80
2,39
35
1,29
0,13
1,28
5
1,76
-1,72
-0,36
36
2,86
-2,71
-0,89
6
5,52
5,51
0,16
37
2,43
2,12
1,19
7
Check Point
38
1,66
1,06
1,28
8
0,70
-0,43
0,55
39
2,01
1,70
-1,07
9
1,96
-0,98
-1,70
40
2,16
2,14
0,26
10
Check Point
41
3,11
0,60
-3,05
11
"Ô38 | -0,23 | -0,30
42
0,95
0,81
-0,49
12
Check Point
43
2,05
-0,49
1,99
13
1,38
-0,80
-1,13
45
Check Point
14
0,85
0,69
-0,49
46
1,74
-1,25
1,21
15
3,15
-2,80
-1,45
47
0,56
-0,26
-0,49
16
3,47
3,36
0,85
48
2,94
2,92
0,37
17
1,71
1,48
-0,85
49
4,44
-4,18
-1,50
18
0,69
-0,62
-0,29
50
4,29
-2,38
3,57
19
1,03
-0,90
0,50
51
2,10
-1,07
-1,80
20
0,72
0,61
0,39
0001
1,24
1,18
-0,37
21
1,03
-0,99
-0,27
0002
1,23
0,98
0,75
22
0,09
0,08
-0,05
0003
1,27
-1,15
0,52
23
1,08
0,28
1,04
0004
0,94
0,84
-0,42
24
0,64
-0,00
0,64
0005
1,22
-1,21
0.20
25
1,82
-0,29
1,80
0006
1,02
-0,95
0,38
26
0,81
-0,77
-0,25
0008
0,75
0,71
-0,24
27
2,42
-2,38
-0,45
0009
1,16
1,04
-0,53
28
2,70
2,10
-1,69
0010
3,15
3,07
0,73
29
0,88
-0,71
0,52
0011
“òdi
-0,04
-0,10
30
1,17
0,27
1,14
0012
0,91
-0,84
-0,35
31
0,90
-0,90
-0,05
0013
2,59
-2,30
1,20
Tab. 1-GCPs Residuals
The calculated total RMSE is 2.10 pixels, 8 m at the ground.
Further residual investigations have been carried out on 5 check
points.
ID
Residual (m)
Res E (m)
Res N (m)
1
28,68
11,52
26,24
7
15,48
1,12
-15,44
10
-15,6
-4,36
-15
12
14,16
-5,76
-12,92
45
10,6
-10,48
-1,48
ü
16,904
6,648
-14,216
Tab. 2- CPs Residual
They show a deteriorated situation basically due to the high
variability of the area height and a low extrapolation capability
of the model (low GCPs redundancy?). According to the results
obtained with other simpler models have driven us to accept the
former; nominal scale mapping our data could be reasonably
good is a 1:50.000 (geometrically speaking).
1:10000 Vector map has been overlaid onto the orthoprojected
image showing, however, a good correspondence (often better
than the estimated accuracy).
```

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