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ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES 
M. Ozkaya 
Bilisim Limited, Bilkent - Ankara, Turkey - 
meral.ozkaya@gmail.com 
Commission IV, WG IV/3 
KEY WORDS: Feature Extraction, Active Contour Models, Ribbon Snake, Ziplock Snake 
ABSTRACT: 
Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for 
different applications such as automated map generation and change detection. Roads are also important to detect other structures 
such as buildings and urban areas. In this paper, the road extraction approach is based on Active Contour Models for 1-meter 
resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was 
separated as salient-roads, non-salient roads and crossings and extraction of these is provided by using Ribbon Snake and Ziplock 
Snake methods. These methods are derived from traditional snake model. Finally, various experimental results were presented. 
Ribbon and Ziplock Snake methods were compared for both salient and non-salient roads. Also these methods were used to extract 
roads in an image. While Ribbon snake is described for extraction of salient roads in an image, Ziplock snake is applied for 
extraction of non-salient roads. Beside these, some constant variables in literature were redefined and expressed in a formula as 
depending on snake approach and a new approach for extraction of crossroads were described and tried. 
1. INTRODUCTION 
Aerial and satellite images contain valuable information about 
geographical structures; the planet's landforms, vegetation, 
natural resources or man-made objects like buildings, roads, 
rail-roads, bridges, etc. This information provided from images 
supports accurate mapping of land cover and make landscape 
features understandable on regional, continental, and even 
global scales. 
In this paper, the road extraction approach is based on The 
Active Contour Models. The Active Contour Models are 
defined by Kass, Witkin, & Terzopoulos (1987). Active 
Contour Models contain Snake Approach. Traditional snake 
model is separated into two representation types as analytic and 
discrete and uses energy minimization rule to detect roads. 
In this study, the road structure was separated as salient-roads, 
non-salient roads and crossings and extraction of these are done 
by using Ribbon Snake and Ziplock Snake. Ribbon Snake and 
Ziplock Snake methods are derived from traditional snake 
model (Laptev, Mayer, Lindeberg, Eckstein, Steger, & 
Baumgartner, 2000) (Neuenschwander, Fua, Szekely, & Kubler, 
1997). 
Salient Roads have a distinct appearance in the image. Thus 
salient roads are roads that are not affected or prevented by 
shadows and occlusion of buildings and trees in the image. 
Detection and verification of roads depend on roads' geometric 
properties such as length, width. Salient roads have steady 
parallel lines that have consistent length and width as 
homogeneity of the corresponding image region. 
Non-salient roads are more difficult to detect. Typical reasons 
of occurrence of non salient roads in an image are shadows and 
occlusion of buildings and trees. To increase the detection rate 
on these types of roads, ziplock snake method is used. 
  
Figure 1: Salient road image 
After extraction of salient and non-salient roads, this 
information is used for crossing detection. Extractions of salient 
and non-salient roads provide not only minimum search space 
for crossing but also some points to detect crossing. Crossings 
link the road network together. Therefore, incomplete salient 
and non-salient roads are potential candidates for crossings. 
Crossing extraction is performed by checking incomplete 
adjacent roads and using the center points of the end of these 
incomplete roads. 
  
Figure 2: Non-Salient road image 
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