# Computer vision-based framework for extracting geological lineaments from
optical remote sensing data

Ehsan Farahbakhsh, Rohitash Chandra, Hugo K. H. Olierook, Richard Scalzo,
Chris Clark, Steven M. Reddy, and R. Dietmar Müller E. Farahbakhsh, R. Chandra
and R. D. Müller are with the School of Geosciences, University of Sydney,
Sydney, NSW 2006, Australia (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>dietmar.muller@sydney.edu.au).R. Scalzo is
with the Centre for Translational Data Science, University of Sydney, Sydney,
NSW 2006, Australia (e-mail: richard.scalzo@sydney.edu.au).H. K. H. Olierook,
C. Clark and S. M. Reddy are with the School of Earth and Planetary Sciences,
Curtin University, Perth, WA 6845, Australia (e-mail:
<EMAIL_ADDRESS><EMAIL_ADDRESS>s.reddy@curtin.edu.au).

###### Abstract

The extraction of geological lineaments from digital satellite data is a
fundamental application in remote sensing. The location of geological
lineaments such as faults and dykes are of interest for a range of
applications, particularly because of their association with hydrothermal
mineralization. Although a wide range of applications have utilized computer
vision techniques, a standard workflow for application of these techniques to
mineral exploration is lacking. We present a framework for extracting
geological lineaments using computer vision techniques which is a combination
of edge detection and line extraction algorithms for extracting geological
lineaments using optical remote sensing data. It features ancillary computer
vision techniques for reducing data dimensionality, removing noise and
enhancing the expression of lineaments. We test the proposed framework on
Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western
Australia using different dimension reduction techniques and convolutional
filters. To validate the results, the extracted lineaments are compared to our
manual photointerpretation and geologically mapped structures by the
Geological Survey of Western Australia (GSWA). The results show that the best
correlation between our extracted geological lineaments and the GSWA
geological lineament map is achieved by applying a minimum noise fraction
transformation and a Laplacian filter. Application of a directional filter
instead shows a stronger correlation with the output of our manual
photointerpretation and known sites of hydrothermal mineralization. Hence, our
framework using either filter can be used for mineral prospectivity mapping in
other regions where faults are exposed and observable in optical remote
sensing data.

###### Index Terms:

Computer vision, Geological lineament, Landsat 8, Gascoyne Province.

##  I Introduction

Digital satellite data with different spatial and spectral resolution are
available for almost every locality on the Earth’s land surface [1, 2, 3, 4,
5]. This enables the procurement of detailed information from surficial
features and processes at different scales. Linear features are considered as
one of the most important surficial features in different fields of study [6,
7, 8]. A linear feature is a two-dimensional, straight or slightly curved
line, linear pattern or alignment of discontinuous patterns evident in an
image, photo or map [9]. Linear features represent the expression of some
degree of linearity of a single or diverse grouping of both natural and
cultural features [9, 10]. Their specific physiographic characteristics make
it possible to detect them due to the tonal change in digital satellite data
[11]. Natural features include any linear features formed by natural processes
such as geological lineaments, drainage networks, and vegetation alignments.
Cultural features comprise man-made features such as road networks, railroads,
and power line corridors [9].
The identification of linear features in remote sensing imagery can be complex
since their spatial and spectral characteristics vary along their extent [9].
A variety of techniques have emerged for extracting linear features from
digital satellite data for different applications such as detecting road
networks [12, 13], stream networks [14, 15], and geological lineaments [11,
16]. Geological lineaments are an expression of the underlying geological
structure and include faults, dykes, shear zones and folds. Linear to
curvilinear faults, dykes and shear zones are of particular interest in
assisting mineral prospecting because of their association with hydrothermal
mineralization [17, 18, 19] but their applications also extend to
hydrogeological [20, 21, 22, 23] and tectonic studies [24, 25, 26]. Faults,
dykes and shear zones may be used to delineate major structural units, analyze
of structural deformation patterns, and identify geological boundaries and
uncover mineral deposits [27, 28, 29, 30, 31]. Mineral deposits are commonly
clustered around specific positions along deep crustal structures, which often
have surface expressions in the absence of sedimentary or regolith cover [32,
33]. Understanding the structural mechanisms for hydrothermal ore deposits
along deep crustal discontinuities are pivotal in terms of economic
considerations but are still in dispute. Some researchers posit that fault
bends are the primary control for ore clusters, while others believe that
fault intersections are most important [34]. Although, both cases are
sometimes important in formation of hydrothermal ore deposits. Irrespective of
the mechanism, detecting geological lineaments can help link surface lineament
expressions to deep-seated structural discontinuities, which ultimately aids
in mineral prospectivity mapping.
Although manual interpretation is effective at identifying geological
lineaments [35, 36], computer vision techniques are required to make this
process efficient. Computer vision is an interdisciplinary field that employs
a wide range of algorithms for gaining a high-level understanding from digital
images or videos. In other words, it seeks to automate tasks akin to what the
human visual system can accomplish [37, 38]. Computer vision is concerned with
automatic extraction, analysis and understanding of useful information from a
single image or a sequence of images. It involves the development of a
theoretical and algorithmic basis to achieve automatic visual understanding
[39]. Some of the applications of computer vision include systems for
automatic inspection, detecting events, modelling objects and organizing
information [39]. In the geosciences, computer vision has diverse applications
including surface modelling [40], rock type classification [41], motion
analysis [42], edge detection and extracting linear features from digital
imagery [43].
The use of computer vision and satellite images to map geological lineaments
has been particularly useful in regional scale studies that are inaccessible,
unsafe or costly to navigate [44, 45]. Traditionally, lineament mapping is
based on a visual or manual photointerpretation. Manual digitizing of
lineaments is subjective, time consuming and expensive [46]. Therefore, a
reliable and standard framework consisting of available computer vision
techniques is needed. This framework must be able to work with different
satellite images representing different spatial and spectral resolutions. The
success of geological lineament extraction procedures depends on the sequence
of applied techniques, and the reliability and accuracy of the edge detection
and line extraction mechanisms [47]. Selection of computer vision techniques,
appropriate edge detection methods and line extraction methods are pivotal in
tectonic linear feature extraction because they exert significant influence on
the accuracy of the final results.
In this study, we present a framework for detecting geological lineaments
using computer vision techniques that include edge detection and line
extraction methods. To demonstrate the validity of this framework, we use
Landsat 8 satellite data and Shuttle Radar Topography Mission (SRTM) digital
elevation models for extracting geological lineaments of the Yinnetharra
1:100,000 map sheet located in the Capricorn Orogen, Western Australia.
Satellite data are subjected to dimensionality reduction, noise removal and
lineament enhancement prior to edge detection and line extraction algorithms.
The extracted geological lineaments are compared to our manual
photointerpretation and geological mapping by the Geological Survey of Western
Australia [48]. We also investigate the correlation between extracted
geological lineaments and known mineral occurrences in the study area. The
final product of our workflow is an efficient evidential data layer for
detecting hydrothermal mineral deposits.
The rest of this paper is organized as follows. In section 2, geological
setting of the study area is investigated. Section 3 reviews different
computer vision techniques which have been applied in this study. Then,
materials and the proposed framework for extracting geological lineaments are
described in section 4. Results of applying the proposed framework are
reported in section 5. The evaluation and limitations of the proposed
framework are discussed in section 6. Finally, Section 7 concludes the paper
with a summary of the future works.

##  II Geological setting of the study area

The Yinnetharra 1:100,000 map sheet is situated within the western portion of
the Capricorn Orogen, known as the Gascoyne Province (Fig. 1) [49]. The
Gascoyne Province, and the wider Capricorn Orogen, record the protracted
amalgamation of the West Australian Craton and subsequent intracontinental
tectono-thermal activity. Two main events have been found to contribute to
forming the West Australian Craton. First, the ca. 2195–2145 Ma Ophthalmia
Orogeny sutured the Glenburgh Terrane (comprised of the Halfway Gneiss) to the
Pilbara Craton [50, 51]. The Opthalmia Orogeny was associated with the
deposition of the Moogie Metamorphics, which was deposited into a foreland
basin that formed as a response to the Glenburgh–Pilbara collision [49].
Second, the ca. 2005–1950 Ma Glenburgh Orogeny amalgamated the combined
Pilbara Craton–Glenburgh Terrane with the Yilgarn Craton to form the West
Australian Craton [49, 52]. The Glenburgh Orogeny was associated with two
major Andean-type granitoid formations, the Dalgaringa and Bertibubba
Supersuites, and several subduction-related basins [52, 53]. After
unification, the Capricorn Orogen experienced at least five intracontinental
tectono-magmatic events, each decreasing in severity of tectonic character and
magmatism [54]. The first two events, the 1830–1780 Ma Capricorn Orogeny and
1680–1620 Ma Mangaroon Orogeny, were both associated with significant
granitoid magmatism of the Moorarie and Durlacher Supersuites, respectively
[55, 56]. Later events were predominantly amagmatic but were still associated
with up to amphibolite-facies metamorphism and hydrothermal activity [57, 58].
Both suturing and intracontinental tectonic events have developed a pervasive
east–west striking structural fabric in the Gascoyne Province that has
compartmentalized the region into several geological ‘zones’ that share
tectonic characteristics [56]. In the south, zone and formation boundaries
trend SW–NE whereas major structures are oriented NW–SE in the north, yielding
a wedge-shaped geometry for the western margin of the Capricorn Orogen (Fig.
1). Compared to the rest of the Capricorn Orogen, the Gascoyne Province is
relatively well exposed, but there are still significant regions of recent
cover that hamper mineral exploration. Known precious and base metal
mineralization predominantly occur along and are associated with structural
discontinuities [49]. A crucial aspect to the mineralization potential in the
Capricorn Orogen is the repeated reactivation of major structures over a
billion years, which allows mineralization to be upgraded to economic scales
[59, 60].

Figure 1: a) Map of the Capricorn Orogen showing the tectonic units and study
area; b) simplified geological map of Yinnetharra 1:100,000 map sheet.

##  III Computer vision techniques

###  III-A Dimension reduction

Dimension reduction is a machine learning method that reduces a set of random
variables by obtaining a set of principle variables [61, 62]. Dimension
reduction techniques can be divided into transformation methods (feature
extraction) and feature selection methods [63]. Transformation methods, such
as Principal Component Analysis (PCA), Independent Component Analysis (ICA)
and Minimum Noise Fraction (MNF), can separate significant and insignificant
parts of the data in addition to reducing data dimensionality. Dimension
reduction techniques are initially used to compress available information
stored in multiple bands into a few bands. In other words, a multispectral
image is converted into a greyscale image preserving maximum information. This
technique can also be applied for different purposes such as statistical
analysis and classification [62].

####  III-A1 Principal component analysis

Principal component analysis is an exploratory analysis method which aims to
find a set of linearly uncorrelated components (principal components) which
could serve as projections from the original data. The first principal
component has the largest possible variance, and each succeeding component in
turn has the next highest variance possible under the constraint that it must
be orthogonal to the preceding components. The mathematical model of the PCA
can be expressed as [64]:

$z=Px$ (1)

where $x$ is a vector consisting of $n$ rows of random variables $x_{1}$,
$x_{2}$,…, $x_{n}$, each $x_{i}$ giving a particular datum that has zero mean,
$P$ is the $n\times n$ standardized orthogonal transformation matrix or
projection square matrix in which each projection vector (row vector) is
constrained to be a unit vector, $z$ is a vector consisting of $n$ rows of
random variables $z_{1}$, $z_{2}$,…, $z_{n}$ projected by $P$ from $x$.
Principal component analysis is one of the techniques that is applied to
transform remote sensing data in order to reveal the most important features
of an image [65, 66]. It is a technique for extracting a smaller set of
variables with less redundancy from high-dimensional data sets in order to
retain as much of the information as possible [67, 68]. Principal component
analysis allows the determination of linear combinations of variables, feature
extraction, dimension reduction, multidimensional data visualization and
underlying variable identification [69]. The principal components are a
projection of the data onto principal axes, known as eigenvectors. A small
number of principal components is often sufficient to account for the majority
of the variance in the data [70, 71].

####  III-A2 Independent component analysis

An alternative method for source separation is independent component analysis
[72, 73, 74]. Unlike PCA, this method both decorrelates input signals and also
reduces higher-order statistical dependencies [75, 76]. Independent component
analysis is a well-established statistical signal/data processing technique
that aims at decomposing a set of multivariate signals into a base of
statistically independent data-vectors with minimal loss of information [77,
78]. Independent component analysis reveals hidden factors that underlie sets
of random variables, measurements, or signals and it attempts to make the
separated signals as independent as possible.
Independent component analysis has a wide range of applications in the field
of signal processing, although it has been less considered as a common
technique in image processing [79]. It is thought that lack of both
comprehensive understanding of the ICA principle, and proper procedures to
interpret the results generated by the ICA, can be one of the main reasons
that ICA is under used in the geosciences, especially for multi- or hyper-
spectral image processing. ICA is a special case of blind source separation
that aims to separate source signals from mixture signals with or without
little prior information about the source signals or the mixing process [80].
The mixture model can be given by [64]:

$x=As$ (2)

where $x$ is a vector consisting of received mixture signals, $s$ is a vector
that contains unknown source signals, and $A$ is an unknown full rank and
invertible mixing matrix. In blind source separation models, the possible
distribution of each source is unknown and we are not interested in, and do
not have prior information about, any source. However, the lack of prior
knowledge about the mixture is compensated for by a statistically strong and
often physically plausible assumption of statistical independence between the
source signals [80]. This assumption provides a possible solution: finding
some signals that are independent from each other which can be mixed to
reproduce the observed signals. Therefore, the aim of ICA is to find a set of
uncorrelated components as independent as possible from each other.
Accordingly, the decomposition model can be given as [64]:

$s=Wx=WAs$ (3)

where $W$ is the unknown unmixing square matrix to be determined. For
simplification of the model without loss of generality, the independent
components and the mixture signals are always assumed to have zero mean and
unit variance. This assumption yields that there is no variance ranking of the
independent components. Via various estimators of independence, there are many
mature algorithms available for implementing ICA. In this study, the Fast ICA,
a fixed-point algorithm that uses an approximation of negentropy as
measurement of independence, is chosen for data processing due to its
computing efficiency, flexible parameters and robustness [76].

####  III-A3 Minimum noise fraction

Minimum noise fraction is similar to the PCA transform and is an effective
technique for reducing a large multi-dimensional data set into a fewer number
of components that contain the majority of information [81]. Unlike a PCA
transform, the resulting axes or components are not necessarily orthogonal,
but are ordered by decreasing signal-to-noise ratio. Moreover, MNF is applied
to isolate noise from signal in a dataset and to determine inherent
dimensionality of an image. Minimum noise fraction is also able to reduce
computational requirements for subsequent processing [82].
The MNF transform implemented in this study involves two cascaded PCA
transformations. The first transformation, based on an estimated noise
covariance matrix, decorrelates and rescales the noise in the data. The second
step is a standard PCA transformation of the noise-reduced data. There can be
as many components as there are input bands; however, each component, starting
from the first, describes less and less of the overall variance of the data
set. Typically, only a small number of components are required to describe
most of the information for the entire data set. The contribution of each
component to the overall information in a multivariate data set (i.e.,
multispectral or hyperspectral imagery) is measured by an eigenvalue. A larger
eigenvalue indicates that a component contains more information from the data
set. The contribution of each band to each component is measured by an
eigenvector, which can be interpreted akin to a correlation coefficient.
Unlike a PCA transform, the resulting axes (components) are not necessarily
orthogonal but are ordered by decreasing signal-to-noise ratio [83].

###  III-B Image enhancement

The image enhancement techniques are applied to increase the quality of image
interpretation based on enhancing edges of features prior to their detection
and extraction. Different types of filtering such as adaptive and
convolutional filtering are part of this suite of techniques.
Adaptive filtering uses the standard deviation of those pixels within a local
box surrounding each pixel to calculate a new pixel value. Typically, the
original pixel value is replaced with a new value calculated based on the
surrounding valid pixels which satisfy the standard deviation criteria [84].
The adaptive filters are particularly adept at preserving image sharpness and
detail while suppressing noise. Lee filter is an adaptive filter which is used
to smooth noisy and speckled data that have an intensity related to the image
scene and that also have an additive and/or multiplicative component [85].
Convolutional filters yield images in which brightness value at a given pixel
is a function of weighted average of surrounding pixels’ brightness.
Convolution of a user-selected kernel with the image array returns a new,
spatially filtered image. Different kernel size and values produce different
types of filters. Standard convolutional filters include high pass, low pass,
Laplacian, directional, median, Sobel and Roberts [69, 86]. In the proposed
framework for extracting geological lineaments, we have applied median and
compared directional and Laplacian filters. A median filter is applied for
smoothing the image, while preserving edges larger than the kernel dimensions
and also to remove speckling noise [87]. This low frequency filter replaces
each center pixel with the median value within the neighborhood specified by
the filter size [86]. A directional filter is a first derivative edge
enhancement filter that selectively enhances image features having specific
direction components (gradients) [88]. The sum of the directional filter
kernel elements is zero. The result is that areas with uniform pixel values
are zeroed in the output image, while those that are variable are presented as
bright edges. A Laplacian filter is a second derivative edge enhancement
filter that operates without regard to edge direction [89]. Laplacian
filtering emphasizes maximum values within the image using a kernel with a
high central value typically surrounded by negative weights in north–south and
east–west directions and zero values at the kernel corners [86].

###  III-C Lineament extraction

Linear features can be divided into two major subclasses that include edges
and lines on a digital greyscale image. An edge in an image is an abrupt
discontinuity in image brightness, which may result from surface boundaries,
shadow boundaries or changes in surface reflectance. A line in an image is a
digital valley or ridge of image brightness [9]. Lines may be formed, for
example, by roads, rivers, and vegetation alignments, as well as by geological
faults and joints. Lineament extraction methods can be conducted via manual
photointerpretation by an expert or (semi-)automatic detection using computer
vision techniques [90]. The automatic methods have resulted in a more
efficient lineament extraction process [91, 92, 93]. A lineament extraction
process comprises two main steps, namely edge detection and line extraction.

####  III-C1 Edge detection

In general, automatic lineament extraction methods are based on edge detection
techniques that enhance the pixels at the edges on an image, instead of
directly extracting edge contours. An edge in an image is defined as a
boundary or contour at which a significant change occurs in some physical
aspect of the image. Linear operators can detect edges through the use of
masks that represent the ideal edge steps in various directions [94]. They can
detect lines and curves in much the same way. Traditional edge detectors were
based on a rather small neighborhood, which only examined each pixel’s nearest
neighbor [95, 96]. This may work well but due to the size of the neighborhood
that is being examined, there are limitations to the accuracy of the final
edge. These local neighborhoods will only detect local discontinuities, and it
is possible that this may cause false edges to be extracted. A more powerful
approach is to use a set of first or second difference operators based on
neighborhoods having a range of sizes and combine their outputs, so that
discontinuities can be detected at various scales [37].
Laplace, zero-crossing and gradient operators are the primary edge detection
techniques [94, 97]. Laplace operators compute some quantity related to the
divergence of the intensity surface gradient of the greyscale images. Zero-
crossing operators determine whether or not the digital Laplacian or the
estimated second direction derivative has a zero-crossing within the pixel
[94]. Gradient operators compute some quantity related to the magnitude of the
slope of the greyscale image wherein the image pixel values are noisy
discretized samples. The major drawback of such an operator is the fact that
determining the actual location of the edge and slope turnover point is
difficult [94]. The frequency and connectivity of the extracted edges by
common edge detection methods are strongly affected by the type and spatial
resolution of the source datasets, signal-to-noise ratio and the parameters of
the edge detection methods [98]. In other words, results of most edge
detection methods involve fragmented edges and should be ultimately
interpreted visually.
An alternative technique is known as Canny edge detection, which is based on
computing the squared gradient magnitude [99]. Local maxima of the gradient
magnitude that are above some threshold are identified as the edges. The
advantages of the Canny edge operator are deriving an optimal operator in the
sense that minimizes the probability of multiply detecting an edge, minimizing
the probability of failing to detect an edge, and minimizing the distance of
the reported edge from the true edge. There is a tradeoff between detection
and localization; the more accurate the detector the less accurate the
localization and vice-versa [94]. An objective function has been designed to
achieve some optimization constraints including maximizing the signal-to-noise
ratio to give perfect detection which favors the marking of true positives;
achieving perfect localization to accurately mark the edges; minimizing the
number of responses to a single edge which favors the identification of true
negatives and prevents marking non-edges [94].

####  III-C2 Line extraction

After edge detection, additional processing must be performed in order to
remove false edge responses and to link gaps between edges, ultimately turning
isolated edges into lines. This processing turns the linearization of the edge
pixels into continuous contours, using certain criteria such as closeness or
some specific geometric property such as the degree of curvature [100].
Edge-linking methods can be classified into two categories, namely local
processing methods and global processing methods [101]. In local processing
methods such as the Hough transform and gradient-based methods, edge pixels
are grouped to form edges by considering each pixel’s relationship to any
neighboring edge pixels [100]. This method is suitable to link edge pixels in
situations where the shape of the edge is unknown. The Hough transform is a
well-known technique which tolerates noise and discontinuities in an image but
it has limitations such as high computing time, unwieldy memory requirements
and limited capability in preserving edge pixel connectivity [11]. The global
processing methods such as pixel connectivity-edge linking use all edge
pixels. Pixels displaying similarities such as same edge geometry are used to
find the best fit of a known shape. The global methods do not need to connect
the edge pixels. However, these methods may miss small pieces of edges or
noise pixels may wrongly be handled as edge segments [102]. The pixel
connectivity-edge linking algorithms have been widely used in applications
which involve extraction of continuous line segments [11].

##  IV Materials and methods

###  IV-A Landsat 8 satellite data

The Landsat 8 satellite carries two instruments including the Operational Land
Imager (OLI) and Thermal Infrared Sensor (TIRS). The OLI comprises nine
spectral bands with a spatial resolution of 30 meters for bands 1 to 7 and 9.
The resolution for band 8 (panchromatic) is 15 meters. The Thermal Infrared
Sensor (TIRS ) provides two thermal bands 10 and 11 that provide more accurate
surface temperatures but are less useful for geological purposes [103, 104].
These sensors both provide improved signal-to-noise radiometric performance
quantized over a 12-bit dynamic range that enables better characterization of
land cover state and condition compared to other Landsat satellites [105].
Moreover, geological features and the geomorphological framework are more
readily distinguished.
The cloud-free Landsat 8 OLI level-2 data product (surface reflectance) is
used in this study and was obtained from USGS EarthExplorer
(https://earthexplorer.usgs.gov). The considered scene was acquired on 13
February 2018. Surface reflectance products provide an estimate of the surface
spectral reflectance as it would be measured at ground level in the absence of
atmospheric scattering or absorption [106]. The scene was pre-georeferenced to
UTM zone 50 south projection with the WGS-84 datum, but all the outputs of
this study are provided in GDA-94. Bands 1 and 9 are not used in this study,
because they have been designed for retrieving atmospheric aerosol properties
and detecting cirrus cloud, respectively [107].

###  IV-B Structural geological maps

Structural geological maps represent the expression of underlying geological
structures exposed at the Earth’s surface [108]. Structural analysis involves
the description of the structural geometry of a deformed field area, kinematic
analysis and dynamic analysis [109]. Descriptive structural analysis is
obtained through detailed field mapping, yielding positions, orientations and
lengths of linear structures, including faults, dykes and fold axes. No
geological map is ever complete but mapping by the Geological Survey of
Western Australia (GSWA) at 1:100,000 is an appropriate benchmark for
assessing the validity of our final workflow results.

###  IV-C Manual photointerpretation

Photointerpretation can be defined as the dialectic and interdisciplinary
integration of personal experience, reasoning, expertise and ground truth
[110]. In manual photointerpretation, fundamental photo-recognition elements
of visual interpretation, as well as their appropriate combinations, are used
[111]. In remote sensing data, manual interpretation is restricted to
analyzing only one or three channels of the satellite data at a time. Further,
our visual acuity does not allow us to identify all spectral differences in
imagery. A human interpreter can only detect and evaluate noticeable
differences in the imagery and she/he cannot carry out repeatable
interpretation work. The manual approach suffers from its inability to deal
quickly with a large quantity of image data. Therefore, visual interpretation
of digital imagery provided by remote sensing platforms does not allow full
exploitation of the data provided [112]. Nevertheless, manual
photointerpretation is another technique that can be used to validate the
proposed semi-automated workflow. Thus, we used manual photointerpretation to
generate a geological lineament map that can be readily contrasted with the
semi-automated extracted lineaments.
The simplest way and first step to work with multispectral data such as
Landsat 8 is to display them as a False Color Composite (FCC). False color
composite images have diverse applications in geological studies such as
discriminating textural characteristics of igneous rocks from those of
sedimentary rocks. Structural features are readily recognizable by applying a
proper RGB color combination of the available bands. Several FCC images can be
generated using the OLI bands, but the ones which assist to discriminate
different lithological units, alteration zones and structural features are the
most important in manual photointerpretation. Therefore, the optimum FCC image
plays an important role in prospecting hydrothermal mineral deposits without
using computer vision techniques [113, 114]. There are different statistical
methods for determining the best FCC image including optimum index factor,
Sheffield index and correlation index [115]. In this study, the correlation
index – known as the most efficient index – is applied for determining the
best FCC image using the MATLAB code available at
https://github.com/intelligent-exploration/IP_MinEx.

###  IV-D Computer vision-based framework for semi-automated extraction of
geological lineaments

A detailed computer vision-based framework for semi-automatic extraction of
geological lineaments from optical remote sensing data is presented in Fig. 2.
This framework starts with acquiring satellite images (Fig. 2a). According to
the level of satellite images, radiometric and geometric corrections are
applied (Fig. 2b). To transform satellite images from multiple bands into a
greyscale (single-band) image, three dimension reduction techniques are used
and compared (PCA, ICA and MNF) using ENVI 5.3 software (Fig. 2c). After image
transformation, Lee and median filters (Fig. 2d, e) are applied respectively
to the output component of the three dimension reduction techniques with the
highest eigenvalue. To enhance edges, directional and Laplacian filters (Fig.
2f) are subsequently applied on greyscale images using ENVI 5.3. The
directional filter is applied using a $3\times 3$ kernel in four directions
with azimuths of $None$, $None$, $None$ and $None$. Using these azimuths, the
directional filter visually enhances the edges striking N–S, NE–SW, E–W and
NW–SE, respectively. The Laplacian filter uses a $3\times 3$ kernel with a
value of 4 for the center pixel and values of -1 for the N–S and E–W pixels.
Edge detection is performed using a Canny edge detector applied on the final
greyscale image (Fig. 2g). Canny edge detection is preferred over Laplace,
zero-crossing or gradient operators because it is resistant to a noisy
environment and signals can be enhanced with respect to the noise ratio by a
non-maxima suppression method which results in one pixel wide ridges as the
output. Lines are then extracted from the binary image produced by the Canny
edge detection process using pixel connectivity-edge linking (Fig. 2h). Pixel
connectivity-edge linking is preferred over Hough transform or gradient based
methods because all pixels identified as linear edge pixels are maintained
[116, 117].
The edge detection and lineament extraction process relies on setting six
different thresholds by the user utilizing PCI Geomatica 2016 software,
including filter radius, edge gradient, curve length, line fitting error,
angular difference and linking distance (Fig. 2j; Table I). Filter radius and
edge gradient threshold are effective for the edge detection and the other
thresholds are applied for the line extraction. Several combinations of the
thresholds within the proposed ranges in Table I were evaluated and various
validation criteria as well as the ground truth were applied to reach the
optimum values which gave a satisfactory result. The Canny edge detection and
pixel connectivity-edge linking methods are applied separately on every four
output images after implementing directional filter (Fig. 5a–5d) for
extracting the lineaments. The extracted lineaments from each image are
combined and a comprehensive lineament map which covers major directions is
generated.

Threshold | Unit | Description | Proposed range | Proposed value (directional filter) | Proposed value (Laplacian filter)
---|---|---|---|---|---
Filter radius | Pixel | Filter radius used in the edge detection | 3-–8 | 5 | 5
Edge gradient | - | Edge gradient value used in the edge detection | 10-–70 | 50 | 10
Curve length | Pixel | Minimum length of a curve to be taken as a lineament | 10-–50 | 50 | 50
Line fitting error | Pixel | Tolerance allowed in curve fitting to form a polyline | 2-–5 | 5 | 5
Angular difference | Degree | Angle not to be exceeded between two polylines to be linked | 3-–20 | 10 | 10
Linking distance | Pixel | Maximum distance between two polylines to be linked | 10–-50 | 50 | 50

TABLE I: Thresholds applied for the edge detection and line extraction in this
study [11, 118].

The filter radius is initially used to determine the number of pixels to be
considered around each pixel for applying the Canny edge detector. The edge
gradient threshold then specifies the values of pixels to be taken as the
edges and those remaining as background. Applying these two steps results in a
binary map product. According to the curve length, this binary map undergoes a
contour number reduction in order to leave just those referring to curves. In
other words, a curve with a number of pixels smaller than that indicated by
the threshold will not be taken into consideration. The resulting curves
generate polylines if they fall within the tolerance defined by the line
fitting error. Finally, two polylines in the simplest case will bind to form a
lineament if their two end points form an angle respecting the value specified
by the angular difference and the distance by the linking distance [118].
According to the spatial resolution of the OLI imagery, streams are the most
important cultural linear features which can be mistaken for geological linear
features. Here, we used Shuttle Radar Topography Mission (SRTM) data with a
spatial resolution of 30 m to delineate streams using the Esri Arc Hydro Tools
(Fig. 2m). The map presented in Fig. 3b is applied to convert a lineament map
(Fig. 2k) to a geological lineament map with minimal cultural lineaments (Fig.
2l–o). A buffer zone with a radius of 5 pixels (150 m) surrounding the streams
is created to prevent errors caused by shifting pixels in SRTM digital
elevation model. Lineaments that overlap the buffer zone by more than half
their length are removed. Therefore, geological rather than cultural
lineaments are more likely to be preserved in the final product (Fig. 2l–o).

Figure 2: Methodology flowchart of this study for semi-automatic extraction of
the geological lineaments. Input data to this flowchart include a digital
satellite image (Landsat 8) and a digital elevation model (SRTM).

##  V Results

###  V-A Optimum false color composite image

The application of correlation index identifies RGB 752 as the best FCC image
for visually discriminating lithological units and structural features, and
manual photointerpretation of the study area (Fig. 3) [119, 120]. Based on the
ranking of available FCC images, RGB 753, 652 and 653 are alternatives.
According to Fig. 3 and extracted geological lineaments using manual
photointerpretation, principal structural linear features strike NW–SE with
subordinate lineaments striking NE–SW in the Yinnetharra region, akin to the
dominant structural trend identified from geological mapping (Fig. 1).

Figure 3: Extracted geological lineaments using manual photointerpretation
draped over false color composite (RGB 752) image of the study area generated
by the OLI bands. In this image, different colors imply the presence of
different lithological units.

###  V-B Comparison of dimension reduction transformations

The output component of the PCA, ICA and MNF with the highest eigenvalue
(including polishing using Lee and median filters on each, Fig. 2) yields
greyscale images presented in Fig. 4. Geological lineaments are recognizable
as dark and bright lines in the three output components (Fig. 4). Pixel values
of the PCA and ICA components show negatively skewed distributions, while the
MNF component shows a positively skewed distribution. The range of pixel
values in the PCA component is much higher than the ICA and MNF components.
Comparison of the components shows that dark and bight pixels as two anomalous
populations of the pixel value distribution have been better separated in the
MNF component.

Figure 4: Output components of the a) PCA, b) ICA and c) MNF techniques with
the highest eigenvalue. These components have been polished by the Lee and
median filters respectively.

Comparison of the different dimension reduction techniques with manual
photointerpretation shows that the MNF component is more robust than the PCA
and ICA components for extracting geological lineaments (Fig. 5). Focusing on
a horizontal profile across a small sector in the west of the Yinnetharra map
sheet reveals that the number of peaks (i.e., positive anomalous pixels) on
the profile obtained from the MNF component correlates well with the
intersected geological lineaments identified via manual photointerpretation.
The MNF profile shows a relatively uniform background signal with strongly
pronounced positive anomalies. In contrast, the graphs obtained from the PCA
and ICA components show intense fluctuation across the profile, showing a
poorer correlation to the lineaments identified via manual
photointerpretation.

Figure 5: a) Geological lineament map provided by manual photointerpretation
draped over a false Landsat 8 color composite image (RGB 752). The
investigated horizontal profile in this map is shown in orange. b–d) The
changes of pixel values relevant to the PCA, ICA and MNF components with the
highest eigenvalue across the profile, respectively.

###  V-C Comparison of directional and Laplacian filters

Application of directional and Laplacian filters on each of the outputs shown
in Fig. 4 reveals geological and cultural lineaments (Fig. 6). Streams and
other large linear features can be readily identified in filtered images.
However, there are some differences between the outputs of applying
directional and Laplacian filters in terms of enhancing some of the geological
lineaments. For example, the Laplacian filter failed to enhance most of the
NW–SE striking geological lineaments located in the southwest of the study
area, which are related to high number of hydrothermal mineral deposits.
Applying a directional filter in different azimuths enhances geological
structures striking in different directions and the results show that it is
more robust in enhancing small lineaments compared to the Laplacian filter.
Small NE–SW striking lineaments located in the northeast of the study area are
probably mafic dykes (Mundine Well Dyke Swarm [121]), and these are better
enhanced using the directional filter compared to the Laplacian filter (Fig.
6a).

Figure 6: Images obtained by applying a) a Laplacian filter and a directional
filter in four directions with azimuths of b) $None$, c) $None$, d) $None$, e)
$None$ on the output component of the MNF technique with the highest
eigenvalue.

###  V-D Extraction of geological lineaments

The most recognizable cultural linear features are streams, which may be
mistaken for geological linear features (Fig. 7). Removal of streams and their
buffer zones yield different geological lineament maps for directional and
Laplacian filters (Fig. 8). To aid visualization and validation, a lineament
density map is produced for each of the filters as well as the manual
photointerpretation and GSWA lineament maps to analyze the dispersion pattern
of the lineaments (Fig. 8).

Figure 7: Extracted streams using SRTM digital elevation model. Figure 8:
Superposition of geological lineaments and hydrothermal mineral occurrences on
density maps resulted from the proposed framework using the MNF component
improved by the a, b) directional and c, d) Laplacian filters; e, f)
geological lineaments mapped by the manual photointerpretation and g, h) the
GSWA. Density maps are created by summing the lineament length available in a
defined grid size. For computational practicality, the size of each grid is
set to 10 pixels (300 m) and the search radius is set to 50 pixels (1500 m).
Lineament density values are taken into fuzzy space using a linear function,
which yields values between 0 and 1 that makes it easier to compare them.

The geological lineament map provided by the directional filter shows a
significantly higher number of the lineaments compared to the map provided by
the Laplacian filter (Fig. 8). Comparison of these with the result of manual
photointerpretation and the GSWA map shows that the major trends of the
geological lineaments have been displayed correctly. The map provided by the
Laplacian filter is more correlated with the GSWA map but the density map of
the geological lineaments extracted using the directional filter is highly
correlated with hydrothermal mineral occurrences (Fig. 8). Areas with high
density values located in the northeast and southwest of the study area (Fig.
8e) are associated with igneous granitic units, which can host diverse types
of economic mineralization [122].

###  V-E Orientation analysis of extracted geological lineaments

The geological lineaments extracted via directional and Laplacian filters
(Fig. 6) reveal similar primary azimuth directions of $None$–$None$, which
account for almost 10% and 12% of all extracted structures, respectively
(Figs. 8, 9, Table II). The directional and Laplacian filters show subtle
differences in subordinate populations, striking at $None$–$None$ and
$None$–$None$, respectively (Fig. 9, Table II).
The NW–SE striking lineaments are strongly correlated with the GSWA map and
manual photointerpretation (Fig. 9). The geological lineaments extracted using
manual photointerpretation shows a major and minor azimuth of $None$–$None$
and $None$–$None$, accounting for almost 15% and 7% of all extracted
geological lineaments, respectively (Fig. 9c, Table II). Geological mapping by
the GSWA reveals a major azimuth of $None$–$None$ that accounts for almost 26%
of all extracted geological lineaments (Fig. 9d, Table II). There is a far
broader spread of NW–SE trending structures for the extracted lineaments using
remote sensing data compared to those mapped by the GSWA. The subordinate
NE–SW striking features identified via semi-automated lineament extraction are
well represented in the manual photointerpretation but are comparatively rare
in the GSWA map (Figs. 8, 9).

Figure 9: Rose diagrams showing the number and orientation of the geological lineaments extracted by the proposed framework using a) directional and b) Laplacian filters applied on the MNF component. As a means of validation, rose diagrams of the geological lineaments mapped by c) manual photointerpretation and d) the GSWA are also shown. Method | Azimuth of the major strike | Percentage | Azimuth of the minor strike | Percentage
---|---|---|---|---
Proposed framework using directional filter | 100–110 | 10 | 20–30 | 6
Proposed framework using Laplacian filter | 100–110 | 12 | 40–50 | 6
Manual photointerpretation | 90–100 | 15 | 20–30 | 7
GSWA geological lineament map | 110–120 | 26 | - | -
TABLE II: Comparison of different geological lineament maps generated by
applying different methods.

###  V-F Correlation of geological lineaments with hydrothermal mineral
occurrences

A strong spatial association is observed between geological lineaments (e.g.,
faults, dykes) and hydrothermal mineral occurrences (Fig. 8). Converting
geological lineaments into a density pattern (Fig. 10) further supports the
notion that geological lineaments and hydrothermal mineralization are co-
located. A density map of the extracted geological lineaments using a
directional filter on the MNF component shows the highest correlation with
hydrothermal occurrence locations (i.e., the highest area under curve in Fig.
10). The result of applying a directional filter shows an even better
correlation with the hydrothermal mineralization compared to the result of
manual photointerpretation. Amongst all the available maps, the GSWA
geological lineament density map shows the lowest correlation with the
hydrothermal mineral occurrences.

Figure 10: Spatial association between hydrothermal mineral occurrences and
different geological lineament density maps. Lineament density values are
taken into fuzzy space using a linear function. The _x_ axis shows the fuzzy
thresholds from 0 to 1. The _y_ axis shows the percentage of known
hydrothermal mineral occurrences that are placed in anomalous areas according
to the thresholds shown in _x_ axis.

##  VI Discussion

###  VI-A Evaluation of semi-automated computer vision framework

In general, evaluation and validation of the different operations used in this
study, against manual photointerpretation of the geological mapping, reveals
an optimum dimension reduction technique, convolutional filter and edge
detection parameter values. Each of these processes are discussed in turn.
Manual photointerpretation of the digital satellite data indicates that
geological mapping by the GSWA captured most, but not all, of the geological
lineaments in the Yinnetharra region of the Gascoyne Province (Fig. 8).
Comparison of the extracted geological lineaments, using the proposed
framework with manual photointerpretation and geological lineament map by the
GSWA, reveals that applying MNF dimension reduction transformation is more
reliable and results are closer to the field observations than the PCA and ICA
(Fig. 4). The primary reason for the superior results from the MNF technique
is high signal-to-noise ratio of the applied component [90, 123] and uniform
behavior of each pixel’s brightness value (positive peaks) facing the
geological lineaments.
Visual (Fig. 8) and statistical (Fig. 9) comparisons show that the extracted
geological lineaments using the proposed framework and applying a Laplacian
filter on the MNF output component with the highest eigenvalue are well
correlated with the GSWA geological lineament map. However, it is clear that
the density map of the extracted geological lineaments using a directional
filter is highly correlated with the hydrothermal mineral occurrences (Fig.
10). Moreover, the result of applying directional filter is similar to the map
provided by the manual photointerpretation. The directional filter is able to
enhance lineaments in desired directions which is an advantage in geological
studies compared to the Laplacian filter. As geologists have prior knowledge
of major structural trends, this approach is valid in regions where sufficient
outcrop exists for field-based studies. Additionally, the images obtained by
applying the directional filter significantly increases the efficiency of
lineament extraction algorithms particularly through detecting small
lineaments [124]. The broader spread of extracted lineament data using both
filters and the manual photointerpretation compared to those extracted using
the GSWA map is attributed to the short length of the extracted geological
lineaments (Fig. 9) [118].
The Canny edge detection and pixel connectivity-edge linking algorithms have
been widely used in different fields [11, 94, 125, 126]. In this study, we
increased the efficiency of these methods for extracting geological lineaments
using the proposed framework. The initial steps of the proposed framework
including dimension reduction techniques, adaptive and convolutional filters
enhance the edges and increase the efficiency of edge detection and line
extraction processes. Moreover, we present an optimum range of parameters
applied in the lineament extraction operation for the Yinnetharra region
(Table I). Optimum values of mentioned parameters may be different for each
study area because of different spectral characteristics in each region, but
our values provide a baseline for future studies. According to the field
observations and geological evidence such as hydrothermal mineral occurrences,
which are highly correlated with the structural features, our proposed
framework is able to extract a high fraction of the geological lineaments
(Fig. 8). A density map of the extracted geological lineaments using the
proposed framework can be provided as the final product and can be used as an
efficient evidential layer for prospecting hydrothermal mineral deposits.
Geological lineament maps provided by field observations are biased by the
mapping geologist, who may have different views in dealing with the same
structural features. The proposed framework is significantly less biased by
the operator, but it is possible to modify the final map by applying new
parameters to approach the desired geological lineament map. Ultimately,
although it is always important to validate results whenever possible to
geological maps, additional lineaments may be revealed through via a semi-
automated computerized framework.

###  VI-B Limitations of the proposed framework and future improvements

The lineament extraction process applied in this study is limited to a
specific curve length, while the geological lineaments show variable lengths
on the Earth’s surface. This semi-automated process extracts geological
lineaments as multipart lines, which may be significantly in error with real
structural discontinuities. Although structural discontinuities may naturally
segmented by fault relays, longer, continuous faults are also likely to be
present [127]. In terranes where regolith or sedimentary cover is a problem,
it is often difficult to differentiate between linked and single faults and,
consequently, most geologists opt for producing single, longer faults as these
require less inference [128, 129]. Applied edge detection and line extraction
methods in the proposed framework can be replaced with recently introduced
methods based on image segmentation for other applications, to overcome above
mentioned limitations [130].
The process of removing streams from the extracted lineaments using the
proposed framework can also be improved by introducing new criteria through
discriminating streams and geological lineaments. Here, an SRTM digital
elevation model was used for mapping streams, but according to the Landsat 8
image (Fig. 3), some bifurcated streams failed to be identified using our
semi-automated framework. High resolution images obtained using UAV-based
photogrammetry can be considered as alternative input data to the proposed
framework to overcome limited spatial resolution of the satellite data for
efficient discrimination of streams and geological lineaments [131].
The proposed framework can be considered as a general methodology, which can
be also used in other geoscientific fields such as hydrogeological and
tectonic studies for mapping geological lineaments. We emphasize that maps
provided using the proposed framework, which show the exposed geological
lineaments on the Earth’s surface and deep-seated faults, can be further
improved using other geophysical techniques, including magnetic, gravity and
even radiometric data.

##  VII Conclusion

In this study, we present a computer vision-based framework for detecting
geological lineaments using optical remote sensing data. The proposed
framework involves diverse techniques for reducing dimensionality, removing
noise and enhancing the lineaments in addition to edge detection and line
extraction. The comparison of different dimension reduction techniques shows
that the extracted geological lineaments using the output component of the MNF
technique are best correlated with the available geological lineament map
provided by the manual photointerpretation.
The Canny edge detector and pixel connectivity-edge linking algorithm are
applied, respectively, to detect edges and to extract lines through providing
a lineament map. The SRTM digital elevation model is applied to remove streams
from the extracted lineaments to produce a geological lineament map. The
extracted geological lineaments are compared to ground truth. Irrespective of
the applied dimension reduction technique and convolutional filter, we observe
a high fraction of the extracted geological lineaments are matched to the GSWA
geological lineament map.
We also investigate the correlation between the density map of the extracted
geological lineaments and known hydrothermal mineral occurrences in the study
area. We conclude that the geological lineaments which are extracted using a
directional filter are highly correlated with the hydrothermal mineral
occurrences, even more than the map provided by the manual
photointerpretation. We demonstrate that the output of the proposed framework
can be applied to create an efficient evidential layer for prospectivity
mapping of the hydrothermal mineral deposits.
In future work, the proposed framework can be used to map geological
lineaments in other regions. The incorporation of various sources of data such
as other types of geophysical maps (e.g., gravity, magnetics, and
radiometrics) can be used to further enhance the framework. Moreover,
statistical methods can be used to optimize parameters applied in lineament
extraction algorithms. This could lead to better the convergence of the rose
diagram obtained from the extracted geological lineaments and the field
observations which are close to the real data.

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