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anbul 2004 
AUTOMATIC ROAD EXTRACTION FROM IRS SATELLITE IMAGES IN AGRICULTURAL AND 
DESERT AREAS 
Uwe BACHER and Helmut MAYER 
Institute for Photogrammetry and Cartography 
Bundeswehr University Munich 
D-85577 Neubiberg, Germany 
Email: {fuwe.bacher, helmut.mayer}@unibw-muenchen.de 
Working Group 111/4 
KEY WORDS: Road Extraction, Fuzzy Logic, IRS, Vision Sciences, Automation 
ABSTRACT 
The appearance of roads in northern Africa differs from that of roads, e.g., in central Europe, which most of the approaches for 
automated road extraction in literature focus on. In this paper we propose a road model for areas with different road appearance in 
IRS satellite image data with a panchromatic resolution of 5 m and 20 m multispectral resolution. We model areas where water makes 
agriculture possible on one hand, and areas dominated by the desert and dry mountainous areas on the other hand. 
In the desert and mountainous areas paved roads appear as more or less distinct lines and the Steger line extraction algorithm can be 
used to extract roads in combination with global grouping. In mountainous areas detected, e.g., in a DEM, much larger curvatures 
are expected to occur than in the desert. In agricultural areas, on which we focus in this paper, roads often do not appear as distinct 
lines. Borders of the fields represented by edges in the image and the knowledge that these borders can be collinearly grouped, possibly 
together with lines, into longer linear structures are used to construct road sections. To close gaps, pairs of lines or edges are connected 
by ziplock snakes. To verify these road sections, the paths of the snakes are evaluated using the line strength and the gradient image. 
The verified road sections are finally globally grouped using the knowledge that roads construct a network between important points. 
Gaps which have a high impact on the network topology are closed if evidence supporting this is found in the image. Results show the 
validity of the approach. 
1 INTRODUCTION 
For the road network in regions consisting in larger parts of desert 
or dry mountainous areas, e.g., in northern Africa, there is either 
no digital data available, or it is often very imprecise and not 
up to date, i.e., incomplete, or even wrong. Because of the large 
areas to be mapped, it is important to use highly automated means 
as well as cheap and readily available data. IRS-1C/D (Indian 
Remote Sensing Satellite) data with a ground resolution of about 
5 m in the panchromatic and about 20 m in red, green, and NIR 
(near infrared) is a good choice for this. We use pan-sharpened 
images. 
The appearance of roads in these regions differs from that of 
roads, e.g., in central Europe, which most of the approaches for 
automated road extraction in literature focus on. In the following 
we give a short overview over related work, focusing on con- 
tributions which employ similar data or similar techniques, e.g., 
snakes, as our approach. 
One of the first approaches to automatic road extraction is (Fis- 
chler et al., 1981), where two types of operators are combined: 
the type I operator is very reliable but will not find all features 
of interest, whereas the type II operator extracts almost all fea- 
tures of interest, but with a large error rate. Starting with the 
reliable type I road parts, gaps are bridged based on the type II 
results employing a search algorithm termed F*. (Wiedemann 
et al., 1998) extract and evaluate road networks from MOMS- 
2P satellite imagery with a resolution similar to IRS employing 
global grouping. The basis of this approach is the Steger line 
operator (Steger, 1998). The use of snakes for the detection of 
changes in road databases in SPOT and Landsat satellite imagery 
1055 
is demonstrated in (Klang, 1998). (Péteri and Ranchin, 2003) 
employ a multiresolution snake based on a wavelet transformed 
image to update urban roads based on given unprecise road data. 
In (Laptev et al., 2000) linear scale space and ziplock-snakes are 
used for the extraction of roads from high resolution aerial im- 
agery. (Dal Poz and do Vale, 2003) propose a semi-automated 
approach for the extraction of roads from medium and high reso- 
lution images based on dynamic programming. Active testing for 
the tracking of roads in satellite images is introduced by (Geman 
and Jedynak, 1996). A semi-automated system for road extrac- 
tion based on dynamic programming and least squares B-spline 
(LSB)-snakes is proposed by (Grün and Li, 1997). The automatic 
completion of road networks based on the generation and verifi- 
cation of link hypotheses given in (Wiedemann and Ebner, 2000). 
(Wallace et al., 2001) present an approach designed for a wide va- 
riety of imagery. It is based on an object-oriented database which 
allows the modeling and utilization of relations between roads as 
well as other objects. Road extraction using statistical model- 
ing in the form of point processes and Reversible Jump Markov 
Chain Monte Carlo is proposed by (Stoica et al., 2004). 
Our approach makes use of the 5 m panchromatic resolution as 
well as the multi spectral information of IRS. It is designed for 
the extraction of roads in mostly agricultural as well as in arid 
areas, the latter also comprising mountainous regions. Section 2 
describes model and strategy. In Section 3 the individual steps of 
the extraction process, namely line / edge extraction, generation 
of connection hypotheses, verification of connection hypotheses, 
and global grouping are detailed. Section 4 presents experimen- 
tal results showing the validity of the approach. An outlook con- 
cludes the paper.