International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 International Archi 
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collinear error function was adopted with the observational 
values of extracted linear primitives and their corresponding 
object space linear primitives (Zuxun Zhang 2003). In this 
paper, a more simple error function is proposed based on the 
generalized point photogrammetry, which unifies the point 
primitives and linear primitives. 
From mathematical all lines or curves are consists of points 
(figure 4) and the collinear equation is commonly used the all 
mathematical points. In this research, the affine transformation 
mathematical model based on parallel ray projection was 
adopted (equation 1), which is the strict in theory and the 
parameter computation of remote sensing image with high 
resolution based on it is very stable, namely the problem of the 
relativity of image parameter calculation is solved completely 
by the strict geometric model (Jianging Zhang 2002). For every 
pair of image space line segment and object space point, only 
one equation, depending on the direction of the line segment, is 
used. When 6 is more than 45°dx can be served as the error, 
otherwise when 6 is less than 45°dy can be served as the error. 
The underlying principle in this mathematical model is that the 
projected point of the object space line by the mathematical 
model (green point) lies on the line segment extracted from the 
image (Figure 4). If we have little GCPs the hybrid adjustment 
can be done without any modification of the error conditional 
equation, which can solve the problem of too little GCPs or 
improper distributing and improve robustness and accuracy of 
adjustment. 
P 
Figure 3: perspective geometry model between the image space 
line segment and the object space feature point on 
line segment 
A ec Lay 
Figure 4: geometry condition between the image space line 
segment and the object space feature point on line 
segment, green point is projected from 
corresponding 3D line, the red point is extracted 
feature point corresponding to 3D line 
  
x=(a, ta, X+a,Y+ az) £78. when [2 > 45° 
^ mcosa 
(1) 
y=b+bX+bY+bZ when —|0|« 45? 
Where x and y are image space coordinate in pixel 
X and Y are object space coordinate in meter 
In term of least squares estimation, equation (1) can be 
considered as a nonlinear observation equation. Applying 
Taylor's series to equation (1), dropping second and higher 
order terms, the linearized form of the observation equation 
becomes 
A, dx A, - da, * A, da, + À, da, 
+4, da, +4,-da+F, =0 
(2) 
A-dy+ A, -db,+ A, -db + A, - db, 
+ A, db, + F, Z0 
4. RESULT 
In this section, results of the approach proposed in this paper 
are presented. We select three SPOT3 PAN images with 10 m 
pixel ground resolution at south of Gansu province of China, 
which belong to mountainous area. The topographic database of 
river net is vectored from map with the scale of 1:50000. The 
DEM data is obtained with the precision of 25m-grid space. 
Because of the non-linearity of the system, the final solution is 
obtained iteratively. The result indicated that the iteration 
converges very fast. After each iteration of the adjustment the 
weight of each observation is recomputed with the weight 
function of Helmert method. Then the weights of the 
observations containing gross errors will become smaller and 
smaller until finally approach zero. Therefore, the result of 
adjustment will not be affected by the blunders. Based on the 
exterior orientation parameter, SPOT3 PAN Images are 
superposed by the georeferenced Map with the scale of 1:50000 
(figure 5). To quantitatively assess the accuracy of the exterior 
orientation parameters computed above, 50 check points for 
every image (ground object points and their corresponding 
points on image) was tested (table 2). The three test area are all 
belong to mountainous area with the elevation interval of 
1000m above and slope grade of 20°above. The conclusion can 
be drawn that the exterior orientation can satisfy the 
requirement of map revision with the scale of 1:50000 based on 
the criterion in table 2 (Remote sensing institute of Sichuan 
province, 2002). 
  
  
Model RMSE X- Y- Max Max 
name RMSE RMSE X-err Y-err 
257281 1.266 0.978 1.500 -1.929 3.471 
  
256282 1.309 1.300 1.318 3.124 -3.036 
  
  
  
  
  
258282 1.485 1.461 1.508 2.608 -2.651 
  
  
  
Table 1: Experiment Results Of Exterior Orientation with 
Check Points (Unit: Pixel) 
  
  
  
  
  
  
  
  
  
Type of Characteristic RMSE | Max Error 
region 
Plain or elevation interval 600m | 20m 2 times of 
Hill grade 71 6° RMSE 
Mountain | elevation interval 2600m 30m 2 times of 
grade 2 6° RMSE 
Table 2: Precision of Check Points for Map Revision with Scale 
of 1:50000 
  
  
Figure 5: . SPOT 
Georefer 
paramet 
This paper introdi 
exterior orientation 
resolution by matcl 
image against the c 
river map or a GI 
based on automatic 
sensing imagery ar 
result shows that 
requirement of maj 
presented procedure 
therefore has the p 
workflow. Propose 
automatic exterior « 
registration of multi 
A( 
Thanks for the sup; 
China (No. 403370: 
Ackermann,F. 1984 
and potential applic 
record, 11(64): 429. 
Burns, J. B., A.R. E 
straight lines, IEE 
Intelligence, Vol. 8, 
Doucette, P., P. Ag 
Self-organised clu: 
imagery, ISPRS | 
Sensing, 55(2001):3 
Gruen, A., P. Agour 
With dynamic progr