JES 
\thens, 
ua.gr 
napping 
ators or 
>tectors, 
erties of 
ines the 
ntation, 
'eologic 
1 image 
llowing 
thwell, 
n were 
Finally, 
igated. 
C). 
Lg. the 
, Meer 
ul tool 
are its 
ven in 
OUGH 
d Hart, 
scenes, 
:ologic 
on and 
ipplied 
n-Cox 
d to a 
| main 
ch was 
ament 
; with 
uch as 
Sobel 
1995; 
'ell as 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
The  semi-automatic and automatic  lineament 
extraction methods, such as edge following, graph 
searching (Wang and Howarth, 1990) and edge 
linking operators (standard and modified Hough 
Transform) (Cross, 1988; Karnieli et.al., 1996; Fitton 
and Cox, 1998), novel edge tracing algorithms (STA, 
START and ALERT algorithm) (Koike et.al., 1998). 
The design of a knowledge-based system, which 
could take the measurable lineament information 
(length, aspect) from a DEM into account (Morris, 
1991). 
n2 
LU 
1.4 Motivation and aim 
From the thorough examination of the literature it is inferred 
that computer-assisted methods for the detection of structural 
(tectonic) lineaments (namely faults and joints), were 
exclusively based on edge enhancement or spatial filtering 
techniques (directional and / or gradient filters). These methods 
produced edge maps requiring further processing (thresholding 
and thinning) for the linear segments to appear with one-pixel 
thickness. Optimal edge detectors (e.g. the Canny algorithm) 
have already been successfully applied on natural scenes with 
quite satisfactory results (binary images with one-pixel 
thickness, efficient length and pixel connectivity), and this 
makes their application in geologic lineament mapping more 
tempting and worth investigating. Furthermore, length is stated 
as a crucial statistical parameter for lineament interpretation 
and classification and optimal edge detection techniques can 
produce segments with sufficient length. 
The implementation of the selected edge detectors on a satellite 
image of a hydrothermal volcanic field has already been 
investigated by the authors (Mavrantza and Argialas, 2003), 
and the results were quite promising. Since only early vision 
operators such as SOBEL, Laplace and Prewitt have been 
applied to date for lineament extraction, further application on a 
DEM is promising. A furthermore comparison of edge 
detection outputs to those of HOUGH Transform with 
evaluation metrics was also required in order to investigate the 
applicability of these methods for lineament extraction. 
2. METHODOLOGY 
2.1 Study area and data used 
For the implementation and quantitative evaluation of the 
applied edge detection algorithms and the HOUGH transform 
for lineament mapping, a geothermal terrain was selected, e.g., 
the Island of Nisyros, which is located in the volcanic back-arc 
of the Dodekanesse Complex, Aegean Sea, Greece. Nisyros is a 
Quaternary strato-volcano, which is characterized by a well 
developed caldera. The major part of this caldera is filled with 
dacitic and rhyodacitic domes, while andesitic and pillow-lavas 
are also present. The tectonic regime of this area is defined by 
two major trends in the NW and in the NE direction 
(PENED99, 2000). 
The data used in the present work were: (a) Band 5 of a 
LANDSAT 5-TM image acquired on August 10, 1991, (b) a 
scanned topographic map with a scale of 1:50.000, and (c) the 
Digital Elevation Model of the same area with 5m spacing. 
791 
2.1 Image pre-processing 
In the pre-processing stage, a LANDSAT 5-TM satellite image 
and the DEM of the same area were geometrically corregistered 
with the scanned topographic map of the same area (with a 
scale of 1:50.000) and geodetically transformed into the 
Transverse Mercator Projection and the Hellenic Geodetic 
Datum (HGRS87). Band 5 of the LANDSAT 5-TM image was 
selected for the implementation of the edge detection 
algorithms, because of its usefulness in lithological and 
structural mapping (Woldai, 1995). This band was initially 
contrast-stretched using a linear transform so as to achieve a 
visually better image for input into the edge detection 
algorithms. 
For the implementation of the Pratt evaluation metric, an 
ancillary ground truth (reference) file was required as input. 
This ground truth file contained all the visually interpreted 
lineaments from the satellite image (and verified on the 
ground), represented with their X, Y coordinates and the total 
number of the actual edge points (in an ASCII format file). 
2.3 Optimal edge detection algorithms: Implementation 
For each algorithm, the combinations of input parameter sets 
were selected based on trial-and-error experiments and assessed 
(a) using mostly the evaluation measures of Pratt and Rosenfeld 
(Abdou and Pratt, 1979; Kitchen and Rosenfeld, 1981) (which 
will be explained in section 2.5.), and (b) by evaluating the 
optical correspondence to the ground map (Figure 1). Due to 
the restricted paper length, only the best results of each 
category are presented. 
  
Figure 1: Left: Initial band TM-5 of the Nisyros caldera. 
Right: Ground map illustrating faults (yellow), 
morphostructural segments delineating the caldera crater 
(light green), coastline (white), and road network (red) (For 
color, see CD). 
For the Canny algorithm, the parameter set with the highest 
score of the Pratt evaluation metric (0.4680) (Table 1) was for 
o=1.25, Tlow=0.40 and Thigh=0.70. 
For the Rothwell algorithm, the following combination of input 
parameters was the optimal: o=2.00, T low=6.00 and a=0.90 
(Figure 2). This parameter set produced a Pratt metric of 0.4508 
(Table 1):