International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
Most research used a number of check points that proved
clearly insufficient for guaranteeing the validity of error results.
One article explained the use if check points from pre-existing
cartography; this procedure is not recommended, as there tends
to be no knowledge about the control map quality itself.
Methods based on GPS constitute the ideal source to obtain
these points, since they yield the coordinates with great
accuracy, and also allow to plan a spatially well-distributed
sample covering the whole area under analysis.
To ensure error reliability, we used a set of 315 randomly
distributed check points whose coordinates were determined by
DGPS techniques. The transformation between the WGS84 and
the UTM local system was achieved by a Helmert
transformation with parameters derived from observation
measurements. These involved between 60 and 90 minutes at
five geodetic vertices around the area, with errors inferior to
0.01 m. After the geodetic frame was determined, and the GPS
processing of the check points adjusted, we were able to
calculate the difference between these points and the elevation
values of the DEM, and estimate the mean error, standard
deviation, and RMSE. The confidence interval of the standard
deviation was also calculated (see Table 3).
S. RESULTS
5.1 Accuracy and reliability results
We constructed 55 DEM from ASTER images. Tables 2
outline the different experimental tests.
A synthesis of the results is given in Table VI, which lists the
values of the mean error (ME), standard deviation (SD), its
confidence interval (CI=95%, 0=0.05), and RMSE. In our case,
Error (m)
Source data Software à b €
ME RMSE SD d
CI
TERRA- Ortho Base 9.7 34,8 28,8 +23
ASTER OrhoEngine -1,5 12,6 125. 0
Cartographie -1,1 7,9 7,8 +0,6
5
o
2
* Mean Error
Root Mean Square Error
Standard Deviation
Confidence Interval for SD (9594)
Table 3. Error statistics for DEMs
the availability of 315 check points enabled the error control to
have a reliability of 96%. This value allows the RMSE
confidence limits to be calculated for each DEM.
Optimal findings include:
» Erdas Imagine generates the most accurate ASTER-
DEM (34.8 m RMSE) using 12 ground control points, a
13x13 correlation window, a correlation threshold value
0.60, defining a TIN structure.
» Geomatica obtains the best ASTER-DEM (12.6 m
RMSE) as a URG structure (30 m cell size), using 15
ground control points., and that the Geomatica Ortho
Engine does not allow changing parameters during the
process.
Based on the results obtained in this study, the generation of
DEM from TERRA-ASTER stereo-images can be done with
methods of digital restitution, leading to RMSE values less than
the pixel size. The sampling interval is one of the factors that
Un
N
influences the quality of the DEM: The best results are obtained
for a cell size twice the pixel size (i.e., 30 m from TERRA-
ASTER). Increasing of this distance among sampled points is
not a good strategy because it 1s equivalent to a progressive
generalization of the DEM structure.
The influence of software is obvious from the experiments
carried out. Erdas Imagine shows worse results from ASTER
data.
These results may require some explaining. We believe that the
main reason is an absence of specific models: Erdas can work
with ASTER data, but it forces to the use of a generic model
unable to take full advantage of the data. Geomatics includes an
ASTER specific model that compensates the shortage of orbital
parameters.
5.2 ASTER DEM versus cartographic DEM
Finally, ASTER-DEM have been compared with the DEM
generated from a topographic map at a 1:25.000 scale. This
process implies the comparison of 2.200.000 points.
Comparing DEM was done by means a simple difference map
algebra operator. Table IV shows the basic statistics. The
accuracy statistics for this data and TERRA-ASTER-DEM are
differents. We emphasize that significant errors affect the
Geomatica ASTER-DEM, even though the mean difference is
similar to one of Erdas ASTER-DEM.
6. CONCLUSIONS
We concluded that along-track TERRA-ASTER provides an
alternative for the extraction of DEM data. In addition, ASTER
data is very attractive because it can be downloaded freely from
its web site and is very affordable. The TERRA-ASTER images
will provide the opportunity for the generation of DEM with
RMSE Z-values less than the pixel size.
Photogrammetric programs for estereocopic spacial data are not
identical. Geomatica shows good ASTER-RMSE values, but
blunders are common. On the other hand, Erdas shows bad
ASTER-RMSE values, but blunders are infrequent.
Ortho Engine, Geomatica, has a specific module for ASTER
data, so the results are better than Erdas module, Ortho Base.
At the moment, this type of programs are improving for every
stereoscopic data like TERRA-ASTER. For example, the last
version the ENVI (4.0) has a specific module for TERRA-
ASTER date. We will continue working in this line, analyzing
the influence in accuracy DEM according to software used, but
we can not conclude about other influential factors.
ACKNOWLEDGEMENTS
This paper is part of the Project 2PRO3A105 co-funded by the
Junta de Extremadura (Consejería de Educación, Ciencia y
Tecnología — 1I Plan Regional de Investigación, Desarrollo
Tecnológico e Innovación de Extremadura) and FEDER (Fondo
Europeo de Desarrollo Regional).
* *
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