Imran Sarwar Bajwa, M. Abbas Choudhry [2006], "A Study for Prediction of Minerals in Rock Images using Back Propagation Neural Networks", in IEEE 1st International Conference on Advances in Space Technologies (ICAST 2006), Aug 2006, Islamabad, Pakistan. pp:185-189

1. Prediction of Minerals by Identifying Various Types of
Rocks using Neural Networks
Imran Sarwar Bajwa1, M. Abbas Choudhary2
1 Faculty of Computer & Emerging Sciences
2Balochistan University of Information Technology and Management Sciences
P.O.BOX – 87300, Quetta, Pakistan
Ph: 0092-81-9201051 Fax: 0092-81-921064
imransbajwa@yahoo.com, abbas@buitms.edu.pk
Abstract Identifying rocks is less critical in some ways than
identifying minerals. A dense, gray mineral is either
This paper presents a novel approach for the galena or it isn't. On the other hand, sandstone can
segmentation of ground based images of rocks using grade into siltstone, limestone into dolostone, gabbro
artificial neural network architecture. Artificial neural into diorite. If a rock is on the borderline between two
network as a color classifier allows a vigorous types, it's usually not all that critical where you place it.
categorization even under various colors saturation
variations, brightness, and non-homogeneous ambient
1.1- Three Great Rock Families
illumination conditions. The designed system actually
identifies the possible minerals by analyzing the surface In Balochistan province most of the area is rocky and
color of the rocks. The rocks in Balochistan are very these rocks are mostly sheer rocks. These rocks do not
hard and defined. Such rocks are typically full of have greenery and these are coloured rocks. According
minerals. The rocks in Balochistan are peculiar in their to various definitions of category these rocks can be
shape and surface colour. Usually, these colours are divided into 3 various categories.
developed due to the reaction of the particles of the
minerals with air. The upper layer if dust upon these • Sedimentary Rocks
rocks can be really useful in identifying the possible • Ingenous Rocks
minerals concealing inside the rocks. The designed • Metmorphic Rocks
mechanism outperforms conventional artificial neural
networks since it allows the network to learn to solve a- Sedimentary Rocks
the task through a dynamic adaptation of its These rocks have clear stratification and they are very
classification context. The designed system is trained by soft and can be easily scratched by a knife. Obviously
providing it the basic information related to the made of particles cemented together and mostly
physical features of various mineral and types of rocks. contains fossils.
Keywords: Minerals Identification, Colour layers in
Rocks, Rock categorization, Colour segmentation.
1. Introduction
Balochistan is the largest province of Pakistan in terms
of area vast over 347,190 Sq. Kilometers. The
Balochistan Plateau extends westward, averaging more
than 1,000 feet in elevation, with many ridges running Figure – 01 Rock composed of generally rounded
across it from northeast to southwest. It is separated pebble-sized clasts cemented together by a finer
from the Indus Plain by the Sulaiman and Kirthar material
ranges. Most of the area of Balochistan is rocky.
Generally, a rock is usually composed of 2 or more
minerals in some physical combination, although some
rocks are composed of only one mineral. Examples of
rocks are limestone, coal, sandstone, granite, or shale.

2. change rapidly at the boundary (edge) of two regions.
Examples of edge detectors are Sobel, Prewitt, and
Roberts [4, 6, 8]. For color images the edge detection
can be performed on color components separately
(such as R, G, and B). These edges are merged to get a
final edged image. Jie and Fei [24] proposed an
algorithm for natural color image segmentation. In this
Figure 02 - Limestone - rock consisting primarily of technique, edges are calculated in terms of high phase
calcite congruency in the gray level image. It uses a K- means
clustering algorithm to label the long edge lines. The
b- Igneous-Volcanic global color information is used to detect approximately
the objects within an image, while the short edges are
Igneous rocks can be further divided into two parts as merged based on their positions.
igneous-volcanic and igneous. Igneous-volcanic rocks
can be defined as they contain numerous bubble-like 1.3 Region growing
cavities that may or may not be lined with minerals and
These techniques find the homogeneous regions in an
they also has obvious bubbly or frothy texture. These
image [5, 8, 21]. Here, we need to assume a set of seed
rocks are fine-grained, uniform in texture, and hard. On
points initially. The homogeneous regions are formed
the other hand igneous-plutonic rocks are made of
by attaching to each seed point those neighbouring
discrete mineral grains locked together (may be
pixels that have correlated properties [12]. This process
loosened by weathering) and they contain large crystals
is repeated until all the pixels within an image are
in a finer-grained mass.
classified. However, the obscurity with region based
approaches is the selection of initial seed points.
Moreover, it is superior to the thresholding method,
since it considers the spatial association between the
pixels [19].
The images which correspond to the measurements of
local homogeneities at different scales are called as ‘J-
Figure 03 - An intrusive, coarse-grained igneous rock images’. The system has the ability to segment color
composed of primarily of quartz and feldspars. textured images without supervision. First the colors
inside the image are quantized to several classes. The
pixels are then replaced by their corresponding color
class label which forms the class map of the image. A
region growing method is then used to segment the
image based on multiscale ‘J-images’.
2- Problem Statement
The rocks in Balochistan are very hard and defined.
Figure 04 - a fine-grained (extrusive) igneous rock Such rocks are typically full of minerals. The rocks in
composed primarily of ferromagnesians with up to 50% Balochistan are peculiar in their shape and surface
plagioclase feldspars colour. Usually, these colours are developed due to the
reaction of the particles of the minerals with air. The
c- Metamorphic Rocks upper layer if dust upon these rocks can be really useful
Metamorphic rocks have a fine texture with an obvious in identifying the possible minerals concealing inside
directional grain (foliation) and they have obvious the rocks. The designed mechanism outperforms
bands, streaks or clumps of different minerals. These conventional artificial neural networks since it allows
rocks are made of mostly of quartz or calcite but is the network to learn to solve the task through a dynamic
coarse-grained and lacks sedimentary features and they adaptation of its classification context.
also contain distinctive metamorphic minerals like 3- Segmentation using neural Networks
garnet or kyanite May often have features of original
rock but is re-crystallized or chemically changed. The field of artificial neural networks has become
enormously fashionable area of research in recent
1.2 Edge Detection years and ANNs have found numerous successful
An edge detector finds the boundary of an object. These applications in almost every field of science and
methods exploit the fact that the pixel intensity values engineering. ANNs can easily handle complicated

3. problems [17] and can identify and learn correlated network should be sufficient to behave as a ship
patterns between sets of input data and corresponding autopilot.
target values. After training, these networks can be
used to predict the outcome from new input data. 4- Study Area and Data Set
Neural Networks mimic the human learning process Balochistan is one of the four provinces of Paksitan. It
and can handle problems involving highly non- is the largest province of Pakistan in terms of area vast
linear and complex data even if the data are imprecise over 347,190 Sq. Kilometers. The Balochistan Plateau
and noisy. They are ideally suited for pattern extends westward, averaging more than 1,000 feet in
recognition and do not require a prior fundamental elevation, with many ridges running across it from
understanding of the process and phenomenon being northeast to southwest. Quetta (the word derives from
modeled [18]. They are also highly suited for Pushtu word kwatta, fort) no doubt is a natural fort,
applications involving parameter varying and/or time surrounded as it is by imposing hills on all sides. The
varying systems. As mentioned above, ship steering encircling hills have the resounding names of Chiltan,
control system is a parameter varying control system, Takatoo, Mordar and Zarghun. Quetta, the capital of
so neural networks have great potential to control Balochistan lies between 300 - 03’ and 300 -27’ N and
this application. 660 - 44’ and 670 - 18’ E. The total geographical area of
There are three main categories of ANNs: Quetta district is 2653 Km2, has a population of almost
feedforward, feedback and cellular neural networks. 1.5 millions and stands at the gateway to central Asia.
It is well established that the feedforward neural Its strategic location has caused rapid population
networks are the most suitable networks for control growth. Following are the type of rocks which has been
applications. In this paper, the Multilayer Perceptron studied during the research.
(MLP) networks have been explored for the
application [19]. A typical MLP network is shown in
Fig1. It consists of an input layer, one or more
hidden layers and an output layer. The number of
hidden layers and the number of neurons in each
layer is not fixed. Each layer may have a different
number of neurons, depending on the application.
Figure 05 - The study area was Quetta and its
Figure 05: A Multilayer Perception Network surroundings as Pishin, Loralai, etc
The output layer neurons may have the same
activation function as the hidden neurons. However,
many applications use a linear function as the
activation function of the output layer neurons. In
other words, the output of each of these neurons is
equal to its net input. A number of researchers [13]
have proved mathematically that an MLP network
with one input layer, one hidden layer and one
output layer is sufficient to approximate any
continuous multivariable function to any desired
degree of accuracy, provided that sufficiently many
hidden layer neurons are available. This suggests Figure 06 – Use images for training Data set of the
that a properly trained single hidden layer MLP designed system etc

4. frame), one hidden layer with 10 neurons, and 7 output
classes, corresponding to dark green, light green,
yellow, light orange, dark orange, brown, red,
blemished and white as a background class.
Figure 07 – Use images for training Data set of the
designed system etc
figure 08 – Overall architecture of the proposed system
To train the network we extracted some pixel examples
of the typical colors (dark green, light green, red,
brown, yellow, light orange, dark orange and white)
using some frames with a graphical interface. A frame
corresponds to a digital image of rocks in white
background. Once trained, the totality of pixels in a
frame was presented to the network (pixel by pixel).
Figure 08 – Use images for training Data set of the
The network returned all image pixels classifieds as one
designed system etc
of the system typical colors. The network classification
5- Used Methodology was stored (pixel by pixel) and we got an output image
from the initial frame. As shown in previous works
Three classes have been defined here according to the [14], simple examples of the colors without brightness
types of the rocks. These three classes of rocks have or saturation examples are enough to obtaining a
been identified on the basis of the colors. Under a range satisfactory classification performance (about 97% with
of proper illumination conditions, the groups of colors low illumination and color saturation variations) with
(orange, green, brown, etc.) can be easily separated by low computational cost.
edges. The problem of color classification can thus be
seen as a problem of determination of optimum edges 6- Conclusion
capable of a suitable partition of an RGB color space.
These edges – capable of processing this separation – The use of an artificial neural network as a color
have some special characteristics: The edges are not classificator allows a robust classification even under
necessarily regular; The edges of each class are not various colours saturation variations, brightness, and
necessarily of same size; The edges must have some non-homogeneous ambient illumination conditions. The
generalization level in such a way that pixels with small approach has proved to be robust with respect to color
variations in color illuminations and saturation are variations and consequently highly applicable to the
evolved by the same edge. proposed domain. It also can easily be applied to
sorting systems of other fruits, such as apples, papayas,
In order to fulfill these requirements, we propose the lemons, etc. Application and test of this methodology
use of an ANN multilayer perceptron (MLP), trained for sorting other fruits remains as future trends of this
using the back-propagation algorithm (Simões, 2000). work.
The adopted network used is shown in Figure 4. In this
network model, there are 3 input neurons (that receive However, the approach presents several characteristics
the triple of color representation of each pixel in a that can restrict future domains: all colors of the domain
must be previously known; the network must be trained

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