Istanbul 2004
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ited Irregular
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol XXXV, Part B2. Istanbul 2004
4.3.2 Geomatica 8.2 with OrthoEngien
OrthoEngine allows work with specific modules for a wide set
of spatial data, including ASTER and SPOT. The DEM may be
generated onlv as an uniform regular grid, URG.
44 DEM generation: extraction of elevations
The automatic extraction of DEM is facilitated if the specific
sensor model information is available. The work flow to
generate each DEM is shown in Figure 2.
In order to guarantee the best possible DEM that can provide
TERRA-ASTER images, we have analyzed the influence of
some aspects, such as number and spatial distribution of GCP,
the data structure (TIN or URG), and the sample interval;
depending on the software used, the algorithms and correlation
coefficient threshold can also be tested.
We have conducted several experiments to determine the
optimal value of influential aspects like number and
distribution of control points; data structure (TIN or URG);
size of grid; and dependind on the software used the algorithms
and correlation coeficient. Finaly, we constructed fifty five
ASTER derived DEM (see Table 2).
N° of N° of
DEM by DEM by
OrthoBase OrthoEngine
; Variable :
Test Range of values
analyzed
OrthoBase: 5...15
l 7 im 11 8
OrthoEngine: 10...16
distribution
2 of Cp 4 distributions 4
a b ] |
3 det TIN */ URG*
structure (only TIN) (only URG)
OB: 100. 80. 60, 40, 20,
4 size ofgrid 15, 10m 7 4
OF: 120.60, 30, 15m
algorithm on ;
se OrthoBasc: diferent size ^
5 of T 13
: of windows [^
matching
coefficient
6 of OrthoBase: 0.6 ...0.95 8 =.
correlation
43 12
DEMs generated: 55
| | DATA [4 PROCESS | | RESULT
Normalization
Internal Orientation (10)
>
External Orientation (EC
*
Stereo-images—p
Ground control
points (GCF)
Automatic extraction of
elevations (DEM)
|
* 3
Accuracy and
quality control
Acccuracy statistics
—+ ME, REMISE,
SD and reliability
Check points—
Figure 2. Common work flow of DEM generation.
* Control Points.
? Triangulated Irregular Network.
* Uniform Regular Grid.
Table 2. Experimental Tests with ASTER images.
4.5 Accuracy and realibility
DEM accuracy is estimated by a comparison with DEM Z-
values, and by contrasting many check points with “true”
elevations. The pairwise comparisons allow the calculation of
the Mean Error (ME), Root Mean Squere Error (RMSE),
Standard Deviation (SD) or similar statistics.
It's obvious that reliability in the process is not a constant but
depends on several factors. The number of chek points is an
important factor in reliability because it conditions the range of
stochastic variations on the SD values (Li, 1991). Another
factor is obvious: The accuracy of check points must be
sufficient for the control objectives.
The estimate of errors in DEM is usually made by following the
USGS recommendation of a minimum of 28 check points (U.S.
Geological Survey, 1997). Li (1991) showed, however, that
many more points are needed to achieve a reliability closer to
what is accepted in most statistical tests. The expression that
relates reliability to number of check points is:
x 100%
2(n —1)
<Equation 1>
Rte) =
where R(e) represents the confidence value in % and n is the
number of check points used in the accuracy test. As an inverse
example, if we wish to obtain a SD confidence value of 5%, we
need about one hundred check points. If we used 28 check
points, we would reach a 20% confidence value.
Therefore, the number of check points must guarantee stability
in error estimates. Revised research is rather heterogeneous
regarding number and accuracy of check points, and no author
has verified reliability in of these results.
