The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
1057
3. IMPLEMENTATION
The proposed method was implemented on the intersection
point cloud produced from HRSC hlOll orbital imagery
(Figure 2). The images cover part of eastern Ares Vallis at
approximately 1.12° to 11.74°N and 335.32° to 336.42°E.
(a) (b) (c) (d)
Figure 2. Left to right: (a) MOLA points, (b) HRSC
intersection points, (c) detected noises (green) over HRSC
intersection points, and (d) re-fed non-noise points (magenta)
over noises (using hlOl 1 ortho-image as background).
3.1 Noise Reduction
First of all, the MOLA points over the same extent were
extracted. As introduced in Section 2.3 they were then
compared with a gridded MOLA DTM and a number of
obvious errors were removed. Meanwhile the hi Oil orbital
imagery was processed through the VICAR software and a 3D
intersection point cloud was derived. A median filter was then
applied to both point clouds and in total 11,884 MOLA points
(Figure 2 (a)) and 28,098 HRSC 3D intersection points (Figure
2 (b)) were available for surface matching.
Prior to commencing the matching, it was critical to decide the
threshold value for determining noise in the HRSC point cloud.
Ebner et al. (2004) studied the height difference between HRSC
intersection points and the MOLA DTM, in which three HRSC
images (h0018, h0022 and h0068) were investigated. Their
results demonstrated the average difference in height between
MOLA DTM and HRSC object points was on the order of 70 m.
Hence an empirical value of 70 metres was adopted as the
threshold value in this paper so these points were flagged as
noise during surface matching and removed from the HRSC
point cloud afterwards.
Surface matching was finished after 11 iterative computations.
As a result, a total of 7,452 points (26.5%) were treated as noise
and removed. In the top diagram of Figure 3, large errors
occurring in the original 3D intersection point cloud are visible
from the side view. However, after surface matching, most of
these errors are successfully removed (Figure 3, bottom).
Moreover, the spatial distribution of the removed noises is
shown in Figure 2 (c) and it can be observed that most of the
errors mainly occurred in two areas. First of all, many errors
appear to exist at the edge of the orbital strip. In addition, when
compared with MOLA points (Figure 2 (a)), it was observed
that the other set of errors were located in the area where there
was a shortage of MOLA points occurring. As noise reduction
was implemented based on the MOLA surface, the HRSC
intersection points might be removed during matching if not
enough information was supplied by the MOLA TIN surface.
This issue was especially obvious in the area of large height
variation, such as the edges of craters and channels. Therefore it
appears that some points being treated as errors might not be
true errors. Further noise inspection was required.
Figure 3. Original 3D point cloud (top) and point cloud after
noise reduction using surface matching (bottom).
3.2 Inspection of Removed Noise
As introduced in Section 2.2, further inspection of removed
noises was carried out in the hi011 ortho-image. The
corresponding pixels of the removed noise point were marked
in the ortho-image and the general topography (e.g. rough or
flat area) at the located pixel was determined. To achieve this,
U. S. Geological Survey (2007) suggested that at least three
pixels were needed to represent the shape of a feature. However,
this paper has adopted a 5 by 5 pixel window due to the
relatively lower image resolution. The window was then