The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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3.6 True 3D Filtering 
Filtering must be used to eliminate mismatched points. Such 
filtering is a classification in correctly matched points. Often, 
the filtering is performed using an interpolation of the terrain 
surface because the end product is a DTM. Thus MATCH-T has 
used a finite element interpolation in order to filter the point 
cloud. This interpolation describes a 2.5D surface. The finite 
element filtering has to choose one Z value for one X,Y 
coordinate pair. It is well suited for DTM extraction but the real 
world is 3D. This method cannot be used to extract 3D Surface 
Models (3DSM): the extracted point cloud of MATCH-T DSM 
delivers a true 3D representation (figure 1.). 
Figure 2. Filtered MATCH-T DSM point cloud distribution 
The new filtering algorithm of MATCH-T DSM works in 3D 
and can select more than one Z for one X,Y coordinate pair. A 
statistical analysis recognizes points with high redundancy and 
then selects those with the best accuracy. The filtering realizes 
both a noise and data reduction without loss of information. 
3.7 Point distribution 
Figure 2 illustrates the 3D point distribution. One can recognize 
that the distribution is similar to an image that has been 
processed with an edge detection operator. Indeed, as MATCH- 
T DSM uses the Forstner operator to extract points, this point 
distribution is as expected. 
After the point filtering the distribution is more regular but 
areas with poor textures are still easy to recognize. 
4. CASE STUDIES 
INPHO has made two case studies using different digital 
camera geometries and different GSDs. The goals have been to 
determine the accuracy, the completeness and the reliability of 
the MATCH-T 3DSM point cloud. In each case, the analysis 
has been made with high resolution images, the image 
orientation parameters have been determined by 
aerotriangulation. 
4.1 Case Study 1: 80/30 compare to 80/60 Overlap 
This case study compares the quality of DSMs extracted from 
two project configurations using the same imagery. The project 
with 80/30 overlap has been derived from the 80/60 project by 
omitting each second strip. 
The information about the project can be found in the table 1. 
Type of terrain 
Urban 
Camera 
Ultra CAMD 
GSD 
7cm 
Spectral 
characteristics 
Panchromatic image 
DSM representation 
3D point cloud 
Number of Control 
points on the ground 
287 
Number of Control 
points off ground 
341 
Table 1. Input information of case study 1 
The result summarized in the table 2 shows clearly the benefit 
of the higher side overlap. The amount of extracted points is 
twice, the final point density is almost 50% higher. With a 
completeness of 93% the DSM covers effectively the complete 
surface. Only poor textured areas are not covered. The precision 
is significantly better and the mean Z offset is considerably 
reduced. Thanks to the high resolution images, the point density 
is very high. Such a point density for photogrammetric products 
is unconventional and opens new fields of research and 
applications. 
Overlap 
80/30 
80/60 
Number of 
extracted points 
264 538 105 
554 846 130 
Number of points 
after the filtering 
13 828 673 
19268617 
Density 
11,93 pts/m 2 
16,62 pts/m 2 
Completeness of a 
50 cm Raster 
85,6% 
92,9% 
Percent of 
validated points 
97% 
97% 
Standard deviation 
of control points 
12,5cm 
10cm 
Mean Z shift 
-6,5 cm 
-2,2 cm 
Table 2. Summary of the results of case study 1