The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
Surface matching is a technique used to carry out co 
registration of point clouds and has been applied broadly in the 
fields of computer vision and geomatics. Its applications can be 
characterised as (i) registration of objects or surfaces comprised 
of 21/2- or three-dimensional point feature data, (ii) detection of 
differences between objects or surfaces, and (iii) integration of 
datasets generated from different sources (Mitchell and 
Chadwick, 1999). By far the most common algorithms used in 
surface matching have been based on some form of least- 
squares adjustment, minimising the differences in position 
between the surfaces during iterative computation. Once the 
matching is finished the transformation parameters are 
computed and the surface is re-aligned to match more closely 
the reference surface. In addition, one of the by-products of 
surface matching is the ability to detect differences, as the 
residuals from the least squares calculation are the surface 
separations. Examination of these differences may reveal actual 
differences that may have occurred between the surfaces 
produced due to the use of different techniques (Pilgrim, 1996). 
Based on this concept, a surface matching technique is 
proposed to determine noise occurring in Mars DTMs. 
The reason most of the terrain features can be seen in the 
optical image is that their colour digital number (DN) values 
vary from neighbouring pixels. That is, for clusters of pixels in 
flat area their DN values should be relatively similar in the 
absence of any significant albedo changes in the area. Based on 
this idea, the noise points being removed from the previous 
stage are reviewed and inspected in the corresponding ortho 
image. Firstly, the corresponding noise pixel was found in the 
ortho-image and the standard deviation of DN values between 
the pixel and its surrounding pixels was then computed. If a 
small difference was observed, the pixel was deemed as being 
located in a flat area. In theory, these points should match well 
with MOLA TIN after surface matching. Therefore, if the point 
is located in a flat area but had a large disparity from MOLA 
after surface matching, the point was confirmed as a noise point. 
According to this rationale, all points being considered as noise 
after surface matching were reviewed. Points whose standard 
deviation of DN values between the surrounding pixels was low 
remained as noise, whilst others were re-fed into the non-noise 
point cloud representing the Martian surface. 
2.3 Workflow 
To implement the surface matching algorithm, the “3D Surf’ 
program was employed. This program was originally developed 
at the University of Newcastle, Australia (Pilgrim, 1991; 
Mitchell, 1994) and was further modified by Buckley (2003) for 
the use of irregular datasets. The detailed algorithm was 
described in Mills et al. (2003). In this paper two point clouds, 
one from 3D intersection points derived from HRSC stereo 
images and the other from a point set from the corresponding 
area measured by the Mars Orbiter Laser Altimeter (MOLA), 
were input into 3D Surf. The former dataset was computed 
using the Video Image Communication and Retrieval (VICAR) 
software (Scholten et al., 2005) while the latter point cloud was 
collected from the MOLA points whose absolute vertical 
accuracy is on the order of 10 m (Ebner et al., 2004). MOLA 
was deemed as the “true” terrain model of Mars and was thus 
input as the reference surface during the matching. To perform 
the comparison, the MOLA points were firstly triangulated 
using a Delaunay triangulation and then a least squares 
minimisation of vertical differences between the HRSC 3D 
intersection point cloud and the corresponding MOLA 
triangulated irregular network (TIN) surface was performed. 
The workflow of this noise reduction process for HRSC DTMs 
is shown in Figure 1. In order to ensure the MOLA points 
adopted in the surface matching were correct, it was compared 
with a gridded global Mars DTM at about 500m spacing (Smith 
et al., 2001) and any points beyond a pre-specified threshold 
were removed if there were any. When the gridded MOLA 
DTM was created such noisy points in the original MOLA point 
cloud were deleted. Moreover, to improve the efficiency of the 
surface matching, a median filter was applied to the MOLA and 
intersection point clouds to reduce any random noise. It is noted 
that once the matching and inspection were finished, the 
reference surface could be replaced by non-noise points. Also, 
the resolution of the intersection point cloud could be increased. 
After updating the two surface models, the matching could be 
performed repeatedly. 
^ f 
Triangulate 
z 
MOLA (TIN) 
Once the matching is finished, the HRSC point cloud is 
transformed and the residuals which represent the disparities 
between the two point clouds are obtained. The points with 
residuals over a pre-specified tolerance value are flagged as 
noise and marked for removal from the HRSC point cloud. 
Noise Inspection in Ortho-image 
Although suspect noise points were removed during the surface 
matching, for several reasons (further discussed in Section 3.1) 
some points being removed are in fact true terrain features. The 
withdrawal of these points could decrease the density and 
representativeness of any final DTM. To improve this situation, 
a method was developed to further examine the points being 
removed. 
MOLA points 
HRSC 
Image matching & 
space intersection 
Intersection 
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Surface 
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Figure 1. Workflow of noise reduction. 
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