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Alan Forghani 
SEMI-AUTOMATIC DETECTION AND ENHANCEMENT OF LINEAR FEATURES TO UPDATE 
GIS FILES 
Alan FORGHANI 
School of Geoinformatics, Planning & Building 
University of South Australia, City East Campus 
PO Box 2471 
Adelaide, SA 5001 
Australia 
Ph: +61-8-83021871 
Fax: +61-9-83022252 
E-mail: alan forghani 9 hotmail.com 
Working Group IV/2 
KEYWORDS: Edge Detection, Thresholding, Mathematical Morphology, Semi-Automatic Linear Feature 
Detection GIS, Mapping, Aerial Photography. 
ABSTRACT 
This paper describes a program developed to allow semi-automatic detection and enhancement of linear features in 
aerial photography. The program employs three different edge detectors namely Sobel, Deriche and Canny as well as 
morphological operations. The effectiveness of each of these operators and mathematical morphology is compared. 
Classification accuracy evaluation demonstrated that the Canny edge filter gave best results among these algorithms. 
The program is able to extract roads, field boundaries, and rivers to update GIS databases. 
1 INTRODUCTION 
Mapping of road networks is a topic of interest to both the Geographic Information Systems (GIS) and remote sensing 
(RS) communities. The problem of keeping road network files up to date is most acute in the urban fringe of major 
urban areas where development processes are most concentrated. 
A program, called the Interactive Linear Feature Detection Program (ILFDP), was developed for semi-automatic linear 
feature detection using different edge detectors and morphological operations (Forghani, 1997a). Three different types 
of spatial filters were employed inthe program: a) noise removal filters b) edge detectors (Sobel, Canny and Deriche) 
and thresholding, and c) mathematical morphologic transformations. 
Aerial images were processed using ILFDP implemented in MATLAB. ILFDP is able to extract edges of roads, field 
boundaries, and rivers. The extracted edges can be used to update GIS databases. 
The purpose of the program is to bring together a set of routines into an easy interactive 'suite' of programs to enable the 
user to optimise edge detection. Different edge detection filters and thresholding values can be compared in order to 
find out which filter and process is most effective and efficient for particular imagery. 
2 BACKGROUND 
Image processing techniques include certain types of image segmentation such as edge enhancement, edge detection 
and mathematical morphology. 
21 Edge Detection 
Edge detection refers to the identification in an image of edges such as object boundaries, or abrupt changes in surface 
orientation and material characteristics (Van Der Hejin, 1995). Trade-offs between edge detectability, noise sensitivity, 
and computational efficiency are often involved in selecting a suitable edge detector for a given application. Edge 
detectors generally suffer from weaknesses not only in sensitivity to noise, but also in poor performance near corners of 
structures (McKeown and Zlotnick, 1990). A successful edge detector for image segmentation depends upon a number 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 289