Discontinuity mapping with automatic lineament extraction from high resolution satellite imagery
ABSTRACT In this study, automatic lineament analysis is performed by using high resolution satellite imagery for identification of rock discontinuities. A case study area is selected as an Andesite mine area in Gölbaı, Ankara, Turkey. For the high resolution data 8-bit Ikonos Precision Plus with 1 meter resolution orthorectified image is used. The image data contain three bands as blue, green, red as band 1, band 2 and band 3, respectively. Then an additional band (fourth band) for the image is assigned by obtaining the average of the three bands. The automatic lineament extraction process is carried out with LINE module of PCI Geomatica v8.2. In order to determine the most accurate parameters of LINE, an accuracy assessment is carried out. To be the reference of the output, manual lineament extraction with directional filtering in four principal directions (N-S, E-W, NE-SW, NW-SE) is found to be the most suitable method. For the comparison of automatic lineament extraction and manual lineament extraction processes, total length and number of lineaments and directional analyses are carried out by constructing the rose diagrams. Besides these, field studies and previous studies carried out in the study area are also taken into consideration. After the accuracy assesssment, final parameters of automatically extracted lineaments are determined. The lineament map produced in this study is found to be a very efficient in mapping the discontinuities for mining applications in terms of cost and time effectiveness.
- SourceAvailable from: Ulf Mallast[Show abstract] [Hide abstract]
ABSTRACT: We describe a semi-automatic method to objectively and reproducibly extract lineaments based on the global one arc-second ASTER GDEM. The combined method of linear filtering and object-based classification ensures a high degree of accuracy resulting in a lineament map. Subsequently lineaments are differentiated into geological and morphological lineaments to assign a probable origin and hence a hydro-geological significance. In the western catchment area of the Dead Sea (Israel) the orientation and location of the differentiated lineaments are compared to characteristics of known structural features. The authors demonstrate that a strong correlation between lineaments and structural features exist, being either influenced by the Syrian Arc paleostress field or the Dead Sea stress field or by both. Subsequently, we analyse the distances between lineaments and wells thereby creating an assessment criterion concerning the hydraulic significance of detected lineaments. Derived from this analysis the authors suggest that the statistic analysis of lineaments allows a delineation of flow-paths and thus significant information for groundwater analysis. We validate the flow-path delineation by comparison with existing groundwater model results based on well data.Hydrology and Earth System Sciences 01/2011; · 3.59 Impact Factor
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ABSTRACT: Subsurface geological fractures in karst terrain are often associated with unpredictable environmental and geotechnical engineering problems. This requires precise mapping and an understanding of the distribution of geological fractures on multi-scales. To extract and investigate surface and subsurface geological fractures on such scales, multi-scales, this study presents two approaches. The first involves geological prediction and visual interpretation of terrain parameters using a digital elevation model (DEM). The second is an automatic detection method using a topographical fabric algorithm that uses a DEM to create a map of ridges, which represent the footwalls of geological fractures, and valleys (channels), which reflect geological fracture zones. Unlike wavelet analysis and the Fourier transform, which use optical remote-sensing images, the integration of visual interpretation and a topographical fabric algorithm is capable of the extraction and spatial correlation of subsurface geological fractures. This method was applied to Kuala Lumpur limestone bedrock in Malaysia, by focusing on the adjacent mountainous areas and the geometries of ex-opencast mining ponds. The spatial correlation of the extracted surface geological fractures was clarified by rose diagrams and semivariogram models. Spatial correlation shows that the Malaysian peninsula, surface and subsurface geological fractures and the geometry of ex-opencast mining ponds share similar trends. The results obtained using this methodology is compared to those of subsurface geological fractures reported by means of geophysical surveying and field investigation. This proposed method may be useful for mapping geological fractures in areas of high soil moisture, where geophysical surveying is difficult and/or not available, and is also highly applicable in other parts of Malaysia or Southeast Asia, permitting a better understanding of the geotectonics and geotechnical engineering setting of the study area.International Journal of Remote Sensing 05/2012; 33(10):3176-3196. · 1.36 Impact Factor
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ABSTRACT: In this paper we present a semi-automatic method to infer groundwater flow-paths based on the extraction of lineaments from digital elevation models. This method is especially adequate in remote and inaccessible areas where in-situ data are scarce. The combined method of linear fil-tering and object-based classification provides a lineament map with a high degree of accuracy. Subsequently, linea-ments are differentiated into geological and morphological lineaments using auxiliary information and finally evaluated in terms of hydro-geological significance. Using the example of the western catchment of the Dead Sea (Israel/Palestine), the orientation and location of the differentiated lineaments are compared to characteristics of known structural features. We demonstrate that a strong correlation between lineaments and structural features exists. Using Euclidean distances be-tween lineaments and wells provides an assessment criterion to evaluate the hydraulic significance of detected lineaments. Based on this analysis, we suggest that the statistical analy-sis of lineaments allows a delineation of flow-paths and thus significant information on groundwater movements. To val-idate the flow-paths we compare them to existing results of groundwater models that are based on well data.Hydrology and Earth System Sciences 02/2011; 15:2665-2678. · 3.59 Impact Factor
DISCONTINUITY MAPPING WITH AUTOMATIC LINEAMENT EXTRACTION FROM
HIGH RESOLUTION SATELLITE IMAGERY
A. Kocala, H. S. Duzgunb, C. Karpuza
a METU, Mining Engineering Department, Inonu Bulvari 06531 Ankara Turkey, firstname.lastname@example.org , email@example.com
b METU, Geodetic and Geographic Information Technologies, Inonu Bulvari 06531 Ankara, Turkey, firstname.lastname@example.org
KEY WORDS: Remote Sensing, Discontinuity Mapping, IKONOS, High-resolution Imagery
In this study, automatic lineament analysis is performed by using high resolution satellite imagery for identification of rock
discontinuities. A case study area is selected as an Andesite mine area in Gölba?ı, Ankara, Turkey. For the high resolution data 8-bit
Ikonos Precision Plus with 1 meter resolution orthorectified image is used. The image data contain three bands as blue, green, red as
band 1, band 2 and band 3, respectively. Then an additional band (fourth band) for the image is assigned by obtaining the average of
the three bands. The automatic lineament extraction process is carried out with LINE module of PCI Geomatica v8.2. In order to
determine the most accurate parameters of LINE, an accuracy assessment is carried out. To be the reference of the output, manual
lineament extraction with directional filtering in four principal directions (N-S, E-W, NE-SW, NW-SE) is found to be the most
suitable method. For the comparison of automatic lineament extraction and manual lineament extraction processes, total length and
number of lineaments and directional analyses are carried out by constructing the rose diagrams. Besides these, field studies and
previous studies carried out in the study area are also taken into consideration. After the accuracy assesssment, final parameters of
automatically extracted lineaments are determined. The lineament map produced in this study is found to be a very efficient in
mapping the discontinuities for mining applications in terms of cost and time effectiveness.
In mining engineering, determination of discontinuities plays an
important role both in design and development stages, which
requires systematic field studies to update the mining plans for
future. It is obvious that these field studies are time and money
consuming operations. Therefore it is important to minimize the
cost and maximize the incomes and also to take into account the
time spent for the field studies.
The term lineament is any linear features that can be picked out
as lines (appearing as such or evident because of contrasts in
terrain or ground cover on either side) in aerial or space imagery
(NASA Remote Sensing Tutorial web page). In geological point
of view, the lineaments are usually faults, joints, or boundaries
between stratigraphic formations. Other types of lineaments
include roads and railroads, contrast-emphasized contacts
between natural or man-made geographic features (e.g., fence
lines). (NASA Remote Sensing Tutorial web page).
Since both Remote Sensing (RS) and Geographic Information
Systems (GIS) have recently started to be used intensively in
many earth science applications, it is believed that such a study
will be useful in terms of following innovative techniques used
in the world for improving nominal mining industry. This study
makes use of RS technology in detecting discontinuities and
their patterns in Gölba?ı andesite mines area.
DATA AND AREA OF RESEARCH
Discontinuities have important role in mining industry. In order
to evaluate them for mine design purposes, they have to be
determined in large scale maps. Therefore using a high
resolution satellite imagery for the lineament extraction is
preferred in this study. For that reason, 8-bit Ikonos Precision
Plus with 1 meter resolution orthorectified image of the andesite
mine area is used. This image combines the details of 1 meter
panchromatic data and color content of 4 meter multi-band data.
The RGB image of the area is given in Figure 1.
The study area covers approximately 15 km2 with coordinates
of 488235 E 4405642 N for the northwest corner and 492338 E
4401956 N for the southeast corner.
It is located 7 km to the east of Gölba?ı, Ankara, Turkey. There
are plenty of working and abondoned andesite mines in the
region. The location map of the study area is given in Figure 2.
Figure 1. RGB Image of the Study Area
Figure 2. Location Map
For lineament extraction, PCI Geomatica Version 8.2 is used.
The most important factor for using Geomatica is the ability to
extract lineaments from images automatically with the LINE
option. For testing the reliability of the software, the lineaments
are extracted manually by directional filtering. Furthermore, the
lineaments detected is compared with those appear in the the
slope face of the mines in the area. The face discontinuities are
determined again by directional filtering and followed by
Automatic Lineament Extraction
LINE option of Geomatica extracts linear features from an
image and records the polylines in a vector segment.
LINE is controlled by the following global parameters:
RADI Radius of filter in pixels
GTHR Threshold for edge gradient
LTHR Threshold for curve length
FTHR Threshold for line fitting error
ATHR Threshold for angular difference
DTHR Threshold for linking distance
The LINE module takes a single image channel as input. If it is
16-bit or 32-bit, the image is first scaled to 8 bit using a
nonlinear scaling routine. The output of the program is a vector
segment which contains linear features as extracted from the
image. If database output channel is specified, a binary edge
image (which is the result of thresholding the gradient) will be
saved in the specified channel.
RADI specifies the size of Gaussian kernel which is used as a
filter during edge detection. The larger the RADI value, the less
noise and less details in the edge detection result.
The thresholding value of the gradient image is given by the
parameter GTHR. This value should be in the range 0 to 255.
The user can experiment with different GTHR values and
choose one which produces a suitable binary image. If the ON
pixels in the image appear to be too sparse, the GTHR value
should be decreased. On the other hand, if the ON pixels are
dense and noisy, the GTHR value should be increased. Note that
it is important to have sufficient information in the edge image
as the subsequent lineament extraction process is based on this
input edge image.
Various other parameters control the line extraction process.
FTHR is the tolerance for fitting line segments to a (curved)
lineament. It is specified in number of pixels. LTHR is the
minimum length of a curve (in pixels) to be considered as
lineament for further consideration. ATHR is the maximum
angle (in degrees) between two vectors for them to be linked.
DTHR is the maximum distance (in pixels) between two vectors
for them to be linked. (PCI Geomatica Manual, 2001)
In this study, the suitable parameter of LINE for rock
discontinuity extraction are determined.
3.1.2. Algorithm of LINE
The algorithm of LINE consists of three stages: edge detection,
thresholding, and curve extraction.
In the first stage, the Canny edge detection algorithm is applied
to produce an edge strength image. The Canny edge detection
algorithm has three substeps. First, the input image is filtered
with a Gaussian function whose radius is given by the RADI
parameter. Then gradient is computed from the filtered image.
Finally, those pixels whose gradient are not local maximum are
suppressed (by setting the edge strength to 0).
In the second stage, the edge strength image is thresholded to
obtain a binary image. Each ON pixel of the binary image
represents an edge element. The threshold value is given by the
In the third stage, curves are extracted from the binary edge
image. This step consists of several substeps. First, a thinning
algorithm is applied to the binary edge image to produce pixel-
wide skeleton curves. Then a sequence of pixels for each curve
is extracted from the image. Any curve with the number of
pixels less than the parameter value LTHR is discarded from
further processing. An extracted pixel curve is converted to
vector form by fitting piecewise line segments to it. The
resulting polyline is an approximation to the original pixel curve
where the maximum fitting error (distance between the two) is
specified by the FTHR parameter. Finally, the algorithm links
pairs of polylines which satisfy the following criteria:
(1) two end-segments of the two polylines face each other and
have similar orientation (the angle between the two segment is
less than the parameter ATHR);
(2) the two end-segments are close to each other (the distance
between the end points is less than the parameter DTHR). (PCI
Geomatica Manual, 2001)
3.1. Manual Lineament Extraction
In order to evaluate the performance of discontinuity map
produced by line module, a reference map is required. The
reference map for performance evaluation is determined based
on manual extraction of lineaments, as suggested in the
literature (Suzen et al., 1998, Koike et al., 1995, Novak et al.,
Mah et al., 1995). The main advantage of manual extraction is
that it is easy to detect the non-geological lineaments such as
roads, fences, field boundaries with human eye.
For manual lineament extraction the image is first smoothed
with an average low pass filter in order to eliminate the noise.
Following the smoothing process, directional filtering method is
selected for the lineament extraction because the directional
nature of Sobel kernels generate an effective and faster way to
evaluate lineaments in four principal directions (Süzen et
The Sobel kernels in four principal directions are given in Table
N-S NE-SW E-W NW-SE
-1 0 1 -2 -1 0 -1 -2 -1 0 1 2
-2 0 2 -1 0 1 0 0 0 -1 0 1
-1 0 1 0 1 2 1 2 1 -2 -1 0
A lineament in an aerial photo or a space image can show up
either darker pixels in the middle and lighter on both sides: or,
is lighter on one side and darker on the other side. When
directional filtering is applied to the image the lineaments show
up in light color which is surrounder by dark colored pixels.
After the filtering operation, the lineaments are digitized
manually with a scale of 1:1500. There are 3357 lineaments
with total length of 130040 meters in the study area.
For the comparison of the manually digitized and automatically
extracted lineaments more effectively, the manually extracted
lineament map is subjected to further.
The raw image contains agricultural fields in the north western
part and village houses in the south western part. Because of
this an area of interest is produced by excluding these areas by
digitizing. In the reference lineament map, that is produced by
manually, a boundary for the area of interest is formed. The
automatically extracted lineaments are overlaid by the reference
data and the lineaments in the restricted areas are deleted.
Not only the field boundaries and village but also the mine
roads are seen as a lineament in the automatic extraction
process. Hence the vectors that corresponds to the roads of the
mines are also deleted. The resultant lineament map produced
by manual digitizing is given in figure 3.
Table 1. Sobel kernels in four principle directions
The automatically extracted lineament maps are overlayed with
the area of interest and the lineaments outside this area are
deleted. The andesite mine roads are easy to detect so the roads
are also deleted. After these processes the length and number of
the automatically extracted maps are found.
The parameters for the LINE process are given Table 2.
Figure 3. Manual Digitized Lineament Map
Table 2. Parameters of LINE Process
Also the number of lineaments are given in Table 3.
Table 3. Results of LINE
In order to verify the methodology’s applicability, the results
obtained from automatic lineament detection need to be
checked. To test the accuracy, two different approaches are
In the first approach rose diagrams for both auttomatically and
manually extracted maps are created and major orientation of
the lineaments are compared.
In order to check the reliability of the results, previous studies in
the region by Karpuz (1982) and field investigations of Gölba?ı
andesite mines are taken into account.
In the second approach, the near-face lineaments with the face
discontinuities are compared. The face discontinuities are
determined with the same method that is applied to the Ikonos
For the accuracy assessment, number and the total length of
lineaments are considered.
5.1. Directional Analysis
For the rose diagram applications, the ‘Line Best Fit’ method is
used. This method uses the direction of a straight line that is
computed with a least squares approximation of a line element.
Simple illustration of the method is given in Figure 4.
Figure 4. Simple illustration of Line Best Fit Method
The rose diagrams of the automatically extracted lineaments are
given in Figure 5.
LINE 2 LINE3
LINE 5 LINE 6
LINE 8 LINE 9
LINE 11 LINE 12
LINE 14 LINE 15
LINE 17 LINE 18
LINE 20 LINE 21
LINE 23 LINE 24
Figure 5. Rose Diagrams of Automatically Extracted
Rose diagram for manual extraction is given in Figure 6.
Figure 6. Rose Diagram of Manually Extracted Lineaments
Rose diagrams generally indicate the similarity in major
discontinuity orientations. The reference lineament map, which
is drawn manually has the major orientation of E-W. Similarly
the automatically extracted lineament maps has the same major
Besides the similarities of the rose diagrams, a reliable site
investigation is necessary for more accurate analysis. A detailed
investigation by Karpuz (1982) is considered for the major
discontinuity orientations. The orientations are found from the
pole distribution data defined by Karpuz (1982). The pole
distribution is shown in Figure 7.
Figure 7. Pole Distribution of the Gölba?ı District
From the pole distribution of the discontinuity sets, three major
discontinuity orientation is determined as N 50º E, E-W, N 70º
W. From these results, it can be easily seen that the results of
Karpuz (1982) and the extracted lineaments have the same
orientation. Except one set that is accepted as the minor
discontinuity set in the field.
From the field studies carried out, the most suitable mine for
detecting lineaments is used for further analysis. As the Ikonos
image from year the 2002 a mine that is abondoned would be
more suitable. Besides this also surface lineaments must be
In order to see the applicability of the methodology in mining
sites, face discontinuity sets are extracted. For the face
lineament extraction, again directional filters are used with the
digital images of the face of the quarry. Except the E-W
direction, all three directions (N-S, NW-SE, NE-SW) are
considered. Because of the reason that nearly horizontal
lineaments are difficult to match with the surrounding
lineaments, those with E-W direction are eliminated.
The face lineaments are determined by the same method, which
is Sobel filtering. The images were taken with a 2 megapixel
digital camera. In order to match the face lineaments with the
surrounding ones filtering the face images in three directions
(N-S, NW-SE, NE-SW) is found to be suitable. Finding a nearly
horizontal lineament in the face can give nothing about the
nearby detected lineaments.
The strikes of the face discontinuity sets determined from face
images, which are supposed to be the lineament orientations
found from Ikonos image, are compared with the orientations of
the lineament map obtained from the Line module. From the
field studies carried out, it is seen that strike directions of face
discontinuity sets matches withe the orientation of the
lineaments detected from nearby the faces.
For the accuracy assessment, the length and the number of the
lineaments are considered. A 15 % of tolerance is accepted for
the matching process. After examining the tolerance values the
final parameters are found.
As the total length of the manually extracted lineaments is
130040 meters. The tolerance value is ±19506 meters. The
lineament maps in this interval are 1 VEC, 2 VEC, 8 VEC, 13
VEC, 16 VEC, 17 VEC, 19 VEC and 23 VEC.
Another parameter is the number of the lineaments. The total
number 3357. similarly a 15 % tolerance is accepted. The
lineament maps in this interval are 1 VEC, 2 VEC, 5 VEC, 14
VEC, 16 VEC, 18 VEC, 19 VEC, 22 VEC, 23 VEC.
From the analyses, an elimination is made according to the
tolerance limits and final parameters are determined according
to the rest of the lineament maps. The final parameters of LINE
are as follows.
GTHR: 25 - 60
LTHR: 20 - 30
For the RADI parameter, also 5 or 7 value gives good results.
Higher values of RADI results in loss of dataand joining of
For the GTHR parameter lower than the determined region
gives plenty of lineaments that appear to be non-geologic. Also
values higher than the region yields to poor results with respect
to the output number and total length.
The LTHR value must not be higher than the region in order not
to obtain circular shapes. Below the lower value curvilinear
lineaments are eliminated.
FTHR parameter is recommended as 2 or 3 in order to get
shorter line segments that better approximate the lineament.
The most suiatable value for ATHR is found to be 20. The
ATHR value less than 20 brings about disconnected lines while
ATHR value higher than 20 results in polygon shaped lines.
The DTHR value determines the maximum distance of two
lineaments to be linked. As the aim of this study is to detect
discontinuity sets with high resolution data, it is better to detect
lineaments seperately. Because of that reason minimum value of
1 is selected.
In this study, decreasing the cost and increasing the efficiency
of detecting discontinuity sets is aimed. Also automatic
lineament extraction yields more effective results. In order to
check the reliability of the automatic lineament extraction,
manual lineament extraction is referred as a reference. From the
results obtained, an elimmination is done within the 15 %
tolerance limit. As a result, final operating parameters for
automatic lineament extraction process of LINE is determined.
Not only the manual extraction of lineaments but also field
studies and previously detected discontinuity sets verify the
As a future application of this study, the accuracy assessment of
the process may be studied detailly in the way that matching the
lineament map according to the location (pixel numbers) of the
Süzen, M.L. and Toprak, V., 1998. “Filtering of Satellite
Images in Geological Lineament Analyses: An Application to a
Fault Zone in Central Turkey”, International Journal of Remote
Sensing, 19(19), pp.1101-1114.
Koike, K., Nagano, S. And Ohmi, M., 1995. “Lineament
Analysis of Satellite Images Using A Segment Tracing
Algorithm (STA)”, Computers and Geosciences, 21(9), pp.
Novak, I. D. And Soulakellis, N., 2000. “Identifying
geomorphic features using Landsat-5/TM Data Processing
Techniques on Lesvos, Greece”, Geomorphology, 34(7), pp.
Mah, A., Taylor, G.R., Lennox, P. and Balia, L., 1995.
“Lineament Analysis of Landsat Thematic Mapper Images,
Northern Territory, Australia”, Photogrammetric Engineering
and Remote Sensing, 61(6), pp. 761-773.
PCI Geomatica Users’ Manual, 2001
NASA Remote Sensing Tutorial Web Page.
Karpuz, C., 1982. Rock Mechanics Characteristics of Ankara
Andesites in Relation to Their Degree of Weathering. PhD
Thesis, Middle East Technical University, Mining Engineering
Department, Ankara, Turkey.
The authors wisteh to thank to INTA Space Imaging Eurasia for
obtaining the image, Middle East Technical University for the
support to this research under the name of BAP-2003-03-05-01
and Middle East Technical University Geological Engineering
Department RS-GIS Laboratory people for their valuable helps