COMPARISON OF EDGE DETECTION AND HOUGH TRANSFORM TECHNIQUES 
FOR THE EXTRACTION OF GEOLOGIC FEATURES 
D. P. Argialas and O. D. Mavrantza 
Laboratory of Remote Sensing, School of Rural and Surveying Engineering, National Technical University of Athens, 
Greece, Heroon Polytechneiou 9, Zografos Campus, 157 80, Athens — rannia@survey.ntua.gr, argialas@central.ntua.gr 
Commission III, WG4 
KEY WORDS: Remote Sensing, Geomorphology, Algorithms, Feature, Edge, DEM, Detection, Extraction, Automation 
ABSTRACT: 
Photointerpretation of geologic lineaments is a subjective process. Therefore there is a need for automation of lineament mapping 
using optimal edge detection techniques. Efforts made in this direction include the application of Sobel and Prewitt operators or 
directional detectors followed by edge linking techniques (e.g. the HOUGH Transform). It is difficult to choose optimal detectors, 
however, since the complex scenes portrayed on satellite images are strongly dependent on the radiometric and physical properties of 
the sensors and on the illumination properties and topographic relief of each scene. Therefore, the geographic region determines the 
"suitability" of an edge detector in geologic feature extraction. In this context, the objective of this work was the implementation, 
evaluation and comparison of selected optimal edge detectors and the HOUGH transform algorithm towards automated geologic 
feature mapping in a volcanic geotectonic environment. The test area was the Nisyros Island (Greece). A LANDSAT 5 - TM image 
and the DEM of the study area were geometrically corregistered with the scanned topographic map of the same area. The following 
edge detectors were applied and assessed on band 5 of the LANDSAT-TM image and the DEM, namely, (a) Canny, (b) Rothwell, 
(c) Black, (d) Bezdek, (e) Iverson-Zucker, (f) EDISON and (g) SUSAN. Modified versions of the HOUGH transform were 
additionally applied to these data. The resulted edge maps were quantitatively assessed with the use of evaluation metrics. Finally, 
the performance and behaviour of each algorithm for geologic feature extraction on the specific geotectonic terrain was investigated. 
N 
Optimal detectors (e.g. the Canny algorithm, etc.). 
3. Operators using parametric fitting models (eg. the 
1l. INTRODUCTION 
Geologic lineament mapping is considered as a very important detectors of Haralick, Nalwa-Binford, Nayar, Meer 
issue in problem solving in Engineering, especially, in site and Georgescu, etc) (Ziou and Tabbone, 1997). 
selection for construction (dams, bridges, roads, etc), seismic 
and landslide risk assessment (Stefouli et.al., 1996), mineral 1.2 Edge Linking Techniques: Overview 
exploration (Rowan and Lathram, 1980), hot spring detection, 
hydrogeological research, etc. (Sabins, 1997). The HOUGH Transform is considered as a very powerful tool 
in edge linking for line extraction. Its main advantages are its 
Lineament photointerpretation is a quite subjective process, insensitivity to noise and its capability to extract lines even in 
requires expertise, training, scientific skills and is time areas with pixel absence (pixel gaps). The Standard HOUGH 
consuming and expensive. Therefore, the need arises for Transform (SHT) proposed by Duda and Hart (Duda and Hart, 
automation of photointerpretation in order to reduce 1972) is widely applied for line extraction in natural scenes, 
subjectivity and to help the analysts. This can be achieved using while some of its modifications have been adjusted for geologic 
computer-assisted techniques, e.g. image processing and lineament extraction purposes (Karnieli, et.al., 1996; Fitton and 
analysis techniques, pattern recognition and expert systems. Cox, 1998). 
1.1 Edge Detection Operators: Overview In the present work, a modified Hough Transform was applied 
in the satellite image and the DEM, namely the Fitton-Cox 
In image processing and computer vision, edge detection treats algorithm. This algorithm has successfully been applied to a 
the localization of significant variations of a gray level image sedimentary terrain covered with prominent joints. Its main 
and the identification of the physical and geometrical properties advantage was the extraction of small line segments, which was 
of objects of the scene. The variations in the gray level image, controlled by the input parameters (Fitton and Cox, 1998). 
commonly include discontinuities (step edges), local extrema 
(line edges) and junctions. Most recent edge detectors are 1.3 Lineament Extraction and Mapping: Overview 
autonomous and multi-scale and include three main processing 
steps: smoothing, differentiation and labeling. The edge Concerning the semi-automatic and automatic lineament 
detectors vary according to these processing steps, to their extraction, there are three main categories of processes: 
goals, and to their mathematical and computational complexity l. The enhancement of geological line segments with 
(Ziou and Tabbone, 1997). the use of linear and non-linear spatial filters, such as 
directional gradients, Laplacian filters, and the Sobel 
In the present work, only step edge detectors were examined, and Prewitt operators (Morris, 1991; Mah et.al., 1995; 
which can generally be grouped into three major categories: Philip, 1996; Siizen, and Toprak, 1998), as well as 
|. Early vision edge detectors (Gradient operators, e.g. morphological filters (Tripathi et.al., 2000). 
the detectors of Sobel and Kirsch). 
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