—S
99). The
Weidner
Surface,
from the
puildings
given by
Order t;
ngs wil
h height
; texture
| Used to
he gray
g pixels
Shapiro,
ice with
second
scribed
s kappa
Sander Oude Elberink
NH NO
CONTRAST= V S 1-05 10157
i=0 j=0
The knowledge that the orientation and shape of man-made objects result in orientated height derivatives allows for the
use of the contrast texture measure to separate buildings and trees. Trees are supposed to have contrast in horizontal,
diagonal, as well in vertical directions. Buildings will show contrast at the edges in only one direction. Therefore, one
can use an anisotropic operation to discriminate between orientated and non-orientated features. The minimum of the
contrast measures for all directions will have an high value at trees and will show low values at buildings, except for
corner pixels (figure 5). In the figure only the horizontal and vertical directions of the contrast measure have been
Fig. 5: Flow chart of the anisotropic contrast measure; a) building and tree in height model; b) co-occurrence contrast
measure in vertical direction; ¢) co-occurrence contrast measure in horizontal direction; d) minimum of two contrast
measures; e) trees extraction, result after morphological 2x2 opening: f) building extraction.
showed. These corner pixels of buildings can be removed by a 2x2 morphological gray value opening. Once the trees
are detected and removed from the height model, buildings will be leftover together with small objects like cars and
bushes (Oude Elberink, 2000).
Further options of isotropic texture measures would be the first and second derivatives of height data. On gable roofs
for example, the first derivative should be constant and close to one, while the second derivative should be zero (Maas,
1999). A general disadvantage of ‘| em ‘;
these measures, however, is their
noise sensitivity (especially in very
dense data sets) and the fact that
they take very high values at
building edges.
3.3 Practical results of the
anisotropic height texture Fig. 6: Left: ariginal DSM; center: trees extraction as a result of the anisolropic
measures contrast measure; eut: pates extraction.
The following examples show the
practical results of the minimum of
contrast measures in four
directions. The density of the laser
Scanner data has a great influence
on the success of the contrast
measure. In cases of 0.5 meter grid
resolution the contrast measure
will give a sufficient extraction of
Fi ig 7: Left: first pulse DSM; center trees extraction as a result of the anisotropic
contrast measure; right: building extraction.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 681