‚ and efficiency 
influencing the 
|! morphologic 
s differentiated 
> information, 
lity of Z and M 
morphology is 
nsequently, the 
2d according to 
> set depends 
of instrument, 
ode(stationary, 
I set depends 
nting error, and 
1g is influenced 
sampling and 
> o 
¥) is the correct 
eight, then 
(2) 
is considered 
he latter part of 
X uncorrelated 
he mean equal 
that f(x) and 
ncorrelated, the 
(3) 
in be estimated 
al approaches. 
etric primitives 
a: 
resentation is 
e morphologic 
Or V(v2/ND (4) 
2 : 
Where V; represent s the discrepancy between true and 
modelled heights, and N, is the total number of points 
on the morphologic feature. 
The mean error Op; of semi automatic representation is 
determined for all the grid points on the modelled 
surface. 
OT zl vy Np (5) 
2 
Where Vj represents the discrepancy between true and 
modelled heights, and AN is the total number of points. 
- The mean error Og opt of optimum representation is 
determined for all the grid points on the modelled 
surface. 
ETC Ny (6) 
2 
Where Yo (represents the discrepancy between true 
and modelled heights, and N, is the total number of 
points. 
For comparison with other tests, the mean error is 
normalised with the maximum height in the represented 
surface H,... 
Geo I. (7) 
- In each experiment , the maximum discrepancy 
between the ideal and the interpolated DTM surface was 
normalised by Hïmaxi, i.e., to have a measure that is 
independent of the height of the primitive: 
MAXER = maximum discrepancy / H ax (8) 
- The sampling efficiency is defined by the number of 
sampled points per unit area: 
E = [Numb. of modelled pts] / [total Numb. of pts] (9) 
4. MORPHOLOGIC MODELLING APPLIED TO 
IDEAL GEOMETRIC PRIMITIVES 
Terrain morphology modelling was applied to some artificial 
ideal geometric primitives. The following rule base was set 
up as a result of these experiments. 
4.1. Semi-spherical features. Terrain morphology 
modelled as semi-spherical surfaces can only be modelled 
via selective modelling when Az/z - 2%, where Az is the 
height of the feature and z is the flying height. Appiying the 
optimum modelling the accuracy was improved by 0.495 to 
1.5%, and the efficiency by 34°% to 77%. 
795 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
    
    
  
    
    
   
    
   
     
    
     
  
    
  
  
    
     
   
  
   
   
   
  
   
    
    
   
      
      
  
   
   
    
  
Figure 4.1. Semi-spherical feature 
4.2. Semi-ellipsoidal features. Terrain morphology 
modelled as semi ellipsoidal surfaces can only be modelled 
via selective modelling when Az/z > 1.5%. Applying the 
optimum modelling the accuracy was improved by 1.7% to 
3.1%, and the efficiency by 3% to 75%. 
  
Figure 4.2. Semi-ellipsoid feature 
4.3. Conical features. Terrain morphology modelled as 
conical surfaces can oniy be modelled via selective 
modelling when Az/z » 2.596. Applying the optimum 
modelling the accuracy was improved by 0.26% to 1.4%, 
and the efficiency by 1196 to 5796. 
  
Figure 4.3. Conical feature 
3.4. Gaussian features. Terrain morphoiogy modelled as 
gaussian surfaces can only be modelled via selective 
modelling when Az/z - 2.0%. Applying the optimum 
modelling, the accuracy was improved by 0.8% to 1.17%, 
and the efficiency reduced by 5% to 10%.