‚ 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%.