Full text: XVIIIth Congress (Part B3)

        
   
   
   
    
    
    
    
   
    
    
   
    
   
   
   
    
   
     
     
    
  
  
    
     
     
     
   
   
i MODELS 
as HRSC (High 
experiment will 
rived products 
3 of the sensor 
defined. Stan- 
ts. Blunt errors 
rmed onto the 
pendent on the 
be closed with 
Ist be obtained 
bit. This image 
s the basis not 
' maps, fly-bys 
as HRSC (High 
le Planet Mars 
bgedeckt. Digi- 
automatisierten 
rdem muß eine 
Jenschnitte mit 
ke von Objekt- 
innt und sofort 
srenzkôrper, in 
er Qualität der 
smethoden ge- 
ken angewandt 
datei mit einer 
uigkeit von 1m 
ıdern auch für 
processing line 
eration, ortho- 
f multispectral 
production of 
eloped for the 
/ of Berlin, De- 
iphy (Prof. Dr.- 
o-Investigators 
    
The Digital Terrain Model (DTM) is the standard form of the 
discrete three-dimensional representation of terrains. The 
DTM generation belongs to one of the key issues in the 
chain of data processing. For many aspects of inter- 
pretation of the image data a DTM is required. Also for en- 
hancing the accuracy of 2D products like orthoimages 
and orthoimage mosaics DTMs are essential. Finally de- 
rived products like contour maps, colour-coded height 
images and fly-by movies can be created with the help of 
DTMs. Because of the wide range of the expected spatial 
resolution of HRSC and WAOSS image data, a global DTM 
derived from WAOSS data is expected as well as local 
DTMs of high resolution using HRSC data. 
First implementations of the photogrammetric software 
were tested with Clementine images of the Moon. Al- 
though the Clementine images are frame-grabber images, 
it was possible to simulate line scanner imagery similar to 
the ones from the HRSC and WAOSS camera. 
The generation of a DTM requires roughly three steps. At 
first the object points in space must be calculated from 
image coordinates of conjugate points determined by the 
digital image matching processes, secondly the object 
points must be transformed into the desired map re- 
ference system, and finally a regular grid of height points 
has to be generated. For this method of generating DTMs, 
which is going to be implemented for the Mars96 Mission, 
the following input is necessary: results from the corre- 
lation of corresponding images and the orientation of the 
camera throughout the recording of the images. Further- 
more a fixed reference body must be well defined. With 
this information a DTM of the area covered by the images 
can be computed. 
2. COMPUTATION OF OBJECT POINTS 
The input for the calculation of object points in space are 
the results of the matching process of two or more 
images. These results are discrete pixel positions of con- 
jugating points in the images. Each pixel in the image data 
corresponds with a unique ray, which is well-defined 
through camera position and pointing, in space. Given 
this information for at least two pixel positions a point in 
space can be calculated through a standard ray inter- 
section. Using more than two images an adequate least- 
square adjustment can be applied. This is very helpful for 
finding blunt errors in the foregoing correlation or in the 
navigation data as well. 
At least two images are needed for the determination of 
conjugate points. But the HRSC/ WAOSS experiment with 
its nine (HRSC) and three (WAOSS) linear CCD arrays will 
provide multiple along-track stereo capability. Even con- 
jugate points in cross-track overlapping can be intro- 
duced to the system. These results together with the 
pointing and position information of the camera for the 
corresponding image lines define rays in space. Due to 
the sensibility of the system towards the orientation of the 
camera, especially its pointing, the navigation parameters 
have to be recorded as accurate as possible and will be 
improved through a photogrammetric bundle block adjust- 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
ment, which is the task of the Technical University of 
Munich, Chair for Photogrammetry and Remote Sensing 
(Prof. Dr.-Ing. H. Ebner) Besides the position and 
pointing accuracy the quality of the matching process 
also has influence on the definition of the object points. 
The discrete object points can then be calculated through 
a standard ray intersection with a least-squares adjust- 
ment technique. Due to the possible number of involved 
images and the possible high matching resolution, the 
computation can be quite intensive. Utilizing least- 
squares adjustment for each single point, blunt errors can 
be detected and the affected points are eliminated. 
The quality of the points depends on the following: 
* quality of the navigation/orbit data 
e quality of the correlation 
e number of used images 
The most important factor defining the quality scale is the 
navigation/orbit data. 
The result of this calculation process is a dense cloud of 
irregularly distributed points in space. 
3. TRANSFORMATION OF OBJECT POINTS 
ONTO PLANET SURFACE 
In order to generate a regular grid of DTM data the 
calculated cluster of points in object space must first be 
transformed into a geographical system using a reference 
ellipsoid. Herefore a standard geodetic transformation is 
utilized. The position and the height of each point can be 
calculated referring to different ellipsoids. While plani- 
metry will be defined on an oblate spheroid the basic 
height reference system for the planet Mars is a triaxial 
ellipsoid. Thereafter the points are transformed into a 
given map projection which defines a rectangular line and 
sample coordinate system. However, they still form an 
irregular grid. 
4. INTERPOLATION OF A REGULAR GRID 
From the irregular grid of map projected object points a 
regular grid has to be derived by means of adequate 
interpolation methods. The following methods are imple- 
mented in the software system: nearest neighbour, sector 
based interpolation, sector based weighted average inter- 
polation (Fig.1) and criging. 
  
Figure 1: Effects of interpolation (gray values = heights). 
Left: interpolation using nearest neighbor. 
Right: interpolation by a sector based weighted approach 
  
    
  
	        
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