The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
Status
Before BA
After BA
Mean (pixel)
1.9
0.0
Maximum (pixel)
9.2
2.6
Standard deviation (pixel)
4.4
0.85
Number of points
500 check points
Table 3. Statistics on back-projection residuals at Columbia
Hills
4. TOPOGRAPHIC MAPPING AT MER LANDING
SITES
4.1 DEM Generation
'•Ì
♦
#
*'■- -i
Legend
Vertical Difference
MB
I-*--»
Figure 6. 3-D surface map of the area of Spirit traverse
We used 644,609 interest points and 1,556,677 grid points to
perform 3-D stereo intersection of ground points based on the
bundle-adjusted EO parameters. Ordinary Kriging with a
spherical semi-variogram model was used to generate a 1-m-
resolution DEM of the area covering the entire traverse of Spirit.
Figure 6 shows the resulting 3-D surface.
4.2 DEM Comparison
To validate the quality of our DEM, we performed a
comparison with a DEM generated by USGS using the same
data. The comparison was done both in horizontal and in
vertical directions. The two DEMs were registered through a 2-
D similarity transformation based on six manually identified
corresponding points. The root mean square error of these
points after the transformation is 0.9 m. After horizontal
registration, a vertical registration was done by a shift and a
rotation in such a way that the average of the vertical
differences was zero. Grid-to-grid vertical differences were then
calculated. The result indicates that most areas have differences
less than 1 meter (Figure 7). The standard deviation of all grid
differences between those DEMs is 0.63 meter which
corresponds to 2 pixels on the image.
Figure 7. Vertical difference between OSU DEM and USGS
DEM
There are a few features with large differences of a few meters.
The most significant one is a small crater in the south west
comer of the DEM. Further investigations will be performed to
figure out the causes of the large differences. The strip artifact,
though smaller than 1 meter in elevation, will also be
investigated. Orthophoto will be generated using the validated
DEM, the original HiRISE imagery and the bundle-adjusted EO
parameters.
5. CONCLUSIONS
In this paper, we presented a rigorous photogrammetric
processing approach for automatic DEM generation from
HiRISE images. Our approach employs a coarse-to-fine
hierarchical matching method that can provide dense and
reliable tie points for both DEM generation and bundle
adjustment. First, interest points were matched up to the level of
original image scale. Then grid points were defined and
matched, also moving from coarse to fine grid scale. For quality
control, an automatic error detection algorithm was incorporated
at each hierarchical level. We evaluated the performance of our
matching results for a test area covering the entire Spirit
traverse. At intermediate levels, the mean residual remained
lower than 0.3 pixel, providing a TIN surface of parallax
difference to provide estimation for dense grid points matching.
At the final level, with 3-pixel grid spacing, the mean residual
was less than 0.11 pixel. We performed a bundle adjustment to
reduce the inconsistencies between HiRISE stereo images. In
the bundle adjustment, second-order polynomials were used to
model the change of EO parameters over time. We chose 500 tie
points from the matched interest points for bundle adjustment.
The performance was evaluated based on back-projection
residuals of the independent check points. The mean residual
was reduced from 1.9 pixels to zero. Also, the standard
deviation of the residuals decreased from 4.4 pixels to 0.85
pixel. We created a DEM of the area covering the Spirit rover
traverse, which was compared with a DEM generated by the
USGS.
ACKNOWLEDGEMENTS
Funding for this research by the NASA Mars Applied
Information Systems Research Program and the NASA Mars
Exploration Program and is acknowledged.
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