bul 2004
edge fea-
[o do so,
re gener-
resulting
ight than
ions with
points on
1 optimal
perty, we
d Ebner,
network
othetical
To form
is calcu-
(3)
/potheses
accepted,
Due to
es has to
potheses
inal road
ned IRS-
'ere used.
different
est areas.
ve evalu-
by visual
e sample
le shows,
>xtracted,
arms, for
ere is the
"ders of a
road net-
"different
correctly
ricultural
expected,
umber of
through a
iedemann
| allowed
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Eus
Figure 7: Result of road extraction in agricultural area
| Figure 8: Image data
Figure 9: Result of road extraction in agricultural area
proach for Semi-Automated Road Extraction from Medium- and
Figure 11: Result of road extraction in mountainous area
S CONCLUSIONS
We have proposed a new approach for the extraction of roads in
agricultural areas in 5 m resolution IRS data. The approach is
based on the fact, that roads in these areas are often not directly
visible, but they run along field borders and form elongated struc-
tures. Starting with the extraction of linear features, connection
hypotheses are generated. The evaluated connections are opti-
mized by means of ziplock snakes. The verification of the gener-
ated road sections is performed by checking the path of the snake.
Using the verified road sections a road network is constructed by
means of global grouping. Experiments have shown, that not only
the main roads but also smaller roads can be extracted from the
used IRS imagery. By means of the approach of (Wiedemann et
al., 1998) we are also able to extract roads in desert and moun-
tainous areas from IRS data.
6 ACKNOWLEDGMENT
We gratefully acknowledge the support by Bundeswehr Geoin-
formation Office (AGeoBw), Euskirchen.
REFERENCES
Dal Poz, A. and do Vale, G., 2003. Dynamic Programming Ap-