A Novel Vision-Based Classification System for Explosion Phenomena

Abstract

The need for a proper design and implementation of adequate surveillance system for detecting and categorizing explosion phenomena is nowadays rising as a part of the development planning for risk reduction processes including mitigation and preparedness. In this context, we introduce state-of-the-art explosions classification using pattern recognition techniques. Consequently, we define seven patterns for some of explosion and non-explosion phenomena including: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds. Towards the classification goal, we collected a new dataset of 5327 2D RGB images that are used for training the classifier. Furthermore, in order to achieve high reliability in the proposed explosion classification system and to provide multiple analysis for the monitored phenomena, we propose employing multiple approaches for feature extraction on images including texture features, features in the spatial domain, and features in the transform domain. Texture features are measured on intensity levels using the Principal Component Analysis (PCA) algorithm to obtain the highest 100 eigenvectors and eigenvalues. Moreover, features in the spatial domain are calculated using amplitude features such as the YC b C r color model; then, PCA is used to reduce vectors' dimensionality to 100 features. Lastly, features in the transform domain are calculated using Radix-2 Fast Fourier Transform (Radix-2 FFT), and PCA is then employed to extract the highest 100 eigenvectors. In addition, these textures, amplitude and frequency features are combined in an input vector of length 300 which provides a valuable insight into the images under consideration. Accordingly, these features are fed into a combiner to map the input frames to the desired outputs and divide the space into regions or categories. Thus, we propose to employ one-against-one multi-class degree-3 polynomial kernel Support Vector Machine (SVM). The efficiency of the proposed research methodology was evaluated on a totality of 980 frames that were retrieved from multiple YouTube videos. These videos were taken in real outdoor environments for the seven scenarios of the respective defined classes. As a result, we obtained an accuracy of 94.08%, and the total time for categorizing one frame was approximately 0.12 s.

Full text

Journal of
Imaging
Article
A Novel Vision-Based Classification System for
Explosion Phenomena
Sumaya Abusaleh *, Ausif Mahmood, Khaled Elleithy and Sarosh Patel
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604, USA;
mahmood@bridgeport.edu (A.M.); elleithy@bridgeport.edu (K.E.); saroshp@bridgeport.edu (S.P.)
*Correspondence: sabusale@my.bridgeport.edu; Tel.: +1-203-543-9085
Academic Editor: Gonzalo Pajares Martinsanz
Received: 4 October 2016; Accepted: 10 April 2017; Published: 15 April 2017
Abstract:
The need for a proper design and implementation of adequate surveillance system for
detecting and categorizing explosion phenomena is nowadays rising as a part of the development
planning for risk reduction processes including mitigation and preparedness. In this context, we
introduce state-of-the-art explosions classification using pattern recognition techniques. Consequently,
we define seven patterns for some of explosion and non-explosion phenomena including: pyroclastic
density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and
sky clouds. Towards the classification goal, we collected a new dataset of 5327 2D RGB images that
are used for training the classifier. Furthermore, in order to achieve high reliability in the proposed
explosion classification system and to provide multiple analysis for the monitored phenomena,
we propose employing multiple approaches for feature extraction on images including texture
features, features in the spatial domain, and features in the transform domain. Texture features
are measured on intensity levels using the Principal Component Analysis (PCA) algorithm to
obtain the highest 100 eigenvectors and eigenvalues. Moreover, features in the spatial domain
are calculated using amplitude features such as the
YCbCr
color model; then, PCA is used to reduce
vectors’ dimensionality to 100 features. Lastly, features in the transform domain are calculated using
Radix-2 Fast Fourier Transform (Radix-2 FFT), and PCA is then employed to extract the highest
100 eigenvectors. In addition, these textures, amplitude and frequency features are combined in an
input vector of length 300 which provides a valuable insight into the images under consideration.
Accordingly, these features are fed into a combiner to map the input frames to the desired outputs and
divide the space into regions or categories. Thus, we propose to employ one-against-one multi-class
degree-3 polynomial kernel Support Vector Machine (SVM). The efficiency of the proposed research
methodology was evaluated on a totality of 980 frames that were retrieved from multiple YouTube
videos. These videos were taken in real outdoor environments for the seven scenarios of the respective
defined classes. As a result, we obtained an accuracy of 94.08%, and the total time for categorizing
one frame was approximately 0.12 s.
Keywords: volcanic eruptions; nuclear explosions; YCbCr; PCA; Radix-2 FFT; SVM
1. Introduction
We define the explosion as a rapid increase in volume, and a release of kinetic energy or potential
energy. Kinetic energy includes radiant, electrical, or thermal energy, while potential energy includes
nuclear or chemical energy. The explosion generates a blast pressure wave or shock wave, high
temperature, and release of gases, in conjunction with loud and sharp sounds caused by the incidents
that are associated with the occurrence of each explosion phenomena.
Explosions can be natural disasters such as volcanic eruptions. A volcano is a spectacular event
in the life of the earth, and it is proof that the earth is alive, active, and ever-changing. Volcanoes
J. Imaging 2017,3, 14; doi:10.3390/jimaging3020014 www.mdpi.com/journal/jimaging

J. Imaging 2017,3, 14 2 of 18
can be classified according to their eruptive style into explosive and effusive eruptions. Typically,
explosive eruptions produce Pyroclastic Density Currents (PDCs), which are among the most complex
and hazardous volcanic phenomena, while effusive eruptions are dominated by the outpouring of lava
onto the ground. Furthermore, some explosive or effusive eruptions produce both lava and tephra
fallout. Tephra is a generic term for any airborne pyroclastic materials accumulation [1–8].
On the other hand, critical man-made disasters are nuclear explosions. According to the location
of the point of detonation in relation to Ground zero, which is the point on the Earth’s surface closest to
the detonation, nuclear explosions can be classified into the following categories: (1) deep underground;
(2) shallow underwater; (3) surface; (4) atmospheric, and high altitude nuclear explosions [9,10].
Nowadays, the emphasis on the risk identification as the first and critical step of the risk
management process is arising. Hence, the development of technology as well as science will lead
to saving lives and properties when they are linked to reliable automatic early warning systems and
effective evacuation procedures.
In a large explosive volcanic eruption at which a volcano vents pyroclastic flows and surges, and
depending on the location of the volcanic eruption, its consequences can be experienced globally or by
an entire hemisphere. Additionally, detecting an explosive volcanic eruption will lead to protecting
citizens not only from primary effects of PDCs that are among the deadliest disasters for populations
living around the volcano, but also from secondary effects of volcanic eruptions that may trigger in
proper conditions lahar, tsunami, and fires. Furthermore, locating the eruption cloud downwind is a
necessity as it is crucial to aviation safety [2,11].
Furthermore, unlike some natural disasters such as fires, hurricanes, tsunami, earthquakes, and
tornadoes where people can rebuild and repair structure in the location of the phenomenon, lava
ejected from an effusive eruption buries agricultural lands, homes, and crops in its path where people
are rarely able to use land buried by lava flows and fountains [12].
2. Problem Statement
2.1. Volcanic Eruptions
A volcanic eruption is the earth’s natural mechanism of cooling off and releasing internal pressure
and heat. Pressure and heat in the crust of the earth cause rocks to melt and form magma that is
stored in a structure called the magma chamber, and it moves onto the earth’s surface through a vent.
Therefore, as magma moves up, it loses dissolved gas and bubbles form in it. This is a driving force
behind eruptions [13–15].
The violence of volcanic eruptions is controlled by two factors including silica content and gas
content. As the silica content of magma is increased, the magma gets more viscous and it becomes
stickier. Consequently, the stickier the magma is, the more viscous the magma, and the more violent
the generated eruption will be, and it becomes more difficult for the gas to escape from magma that
is highly viscous. The gas content of the magma is the second factor. Hence, the more gas, the more
violent the eruption will be, while the less gas, the less violent the eruption will be [14].
An explosive eruption may produce a Pyroclastic Density Current (PDC) phenomenon which is
a moving mixture of hot gases and pyroclastic materials that flow over the ground (gravity-driven
deposits that fall down the volcano slopes) [
5
]. The PDC phenomenon [
16
] may generate from the
collapse of parts of an eruption column following explosive disintegration of magma and rocks
in a volcanic conduit (e.g., Figure 1a [
17
]), or from hot avalanches derived from lava domes (e.g.,
Figure 1b [
18
]), or from laterally inclined blasts (e.g., Figure 1c [
19
]), or when the ejected pyroclastic
mixture is unable to mix efficiently with the surrounding atmosphere and spreads laterally (termed
the boiling-over regime, e.g., Figure 1d [20]).
On the other hand, effusive eruptions such as a fissure volcano typically generate lava fountains
and flows. Lava color depends on its temperature, and the process of turning heat energy into light is
called incandescence [21] (e.g., Figure 1e [22]).

J. Imaging 2017,3, 14 3 of 18
Moreover, lava and tephra fallout products can be deposited together directly by explosive or
effusive eruptions. Consequently, tephra is a generic term for any airborne pyroclastic accumulation
such as: blocks, bombs, cinder/scoria/lapilli, coarse ash, and fine ash (dust), etc. (e.g., Figure 1f [
23
]).
J. Imaging 2017, 3, 14 3 of 18
Moreover, lava and tephra fallout products can be deposited together directly by explosive or
effusive eruptions. Consequently, tephra is a generic term for any airborne pyroclastic accumulation
such as: blocks, bombs, cinder/scoria/lapilli, coarse ash, and fine ash (dust), etc. (e.g., Figure 1f [23]).
(a) (b) (c)(d)(e)(f)
Figure 1. (a) steam-driven eruption column of Guagua Pichincha, 10 km west of Quito, Ecuador, on 7
October 1999 (photo credit: Department of the Interior/U.S. Geological Survey) [17]; (b) collapse of a
lava dome generates pyroclastic flows on Sinabung volcano, Sumatra, Indonesia, in 2013 (photo
credit: Tom Pfeiffer/www.VolcanoDiscovery.com) [18]; (c) PDC generated by a lateral blast during
the main eruption of Mount Saint Helens, Washington State, USA, on 18 August 1980 (photo credit:
Department of the Interior/U.S. Geological Survey) [19]; (d) boiling over-based PDC generation
mechanism from an undersea volcano erupted off the coast of Tonga, on 18 March 2009 (photo credit:
the image was extracted from a video on YouTube website) [20]; (e) lava lake in Halema’uma’u crater,
at the summit of Kilauea volcano, on 26 May 2016 (photo credit: Department of the Interior/U.S.
Geological Survey) [22]; and (f) lava and tephra fallout from the Eruption of Pu‘u ‘Ö‘ö, Kilauea,
Hawaii, in 1984, tephra fallout in this example is a combination of cinder, Pele’s tears, and Pele’s hair
(photo credit: Department of the Interior/U.S. Geological Survey) [23].
2.2. Nuclear Explosions
Typically, nuclear explosions including deep underground, shallow underwater, surface, and
atmospheric, form a mushroom-shaped cloud. In contrast, the high-altitude explosions form an
artificial aurora display with ionized region [9,24]. In this research, our focus will be on identifying
nuclear explosions which form a mushroom-shaped cloud.
The formation of a nuclear mushroom-shaped cloud can be described through an example of a
shallow underwater explosion, the Baker test, which was conducted at Bikini Lagoon in 1946. At the
beginning of the explosion, the water near the explosion was illuminated by the fireball formation.
However, the water waves caused distortion on the surface of the lagoon that prevented a clear view
of the fireball. The hot gas bubble underwater initiates a shock wave. Intersection of the shock wave
with the surface produces a slick, which is a ring of darkened water that is rapidly expanding, while
the reflection of the water shock wave at the surface causes a crack. A crack is a white circular patch
behind the dark region. Then, a spray dome (a column of broken water and spray) is thrown up over
the point of burst. The sides of the spray dome become steeper when the water rises. The disturbance
created by the underwater burst causes a series of waves to move outward from the center of the
explosion across the surface of the lagoon, where the test was conducted. The water flowed into the
cavity as the pressure of the bubble was released, which caused the water to be thrown up as a hollow
cylinder, or chimney of spray, called the “column/plume”. The radioactive contents of the bubble
were vented through the hollow column and formed a cauliflower-shaped cloud at the top in a
shallow underwater explosion, which concealed part of the upper portion of the column. It contained
some of the fission products, weapon residues, and a large amount of water in droplet form. Figure 2
depicts the mushroom-shaped cloud of the underwater Baker nuclear explosion [25].
Figure 1.
(
a
) steam-driven eruption column of Guagua Pichincha, 10 km west of Quito, Ecuador, on
7 October 1999
(photo credit: Department of the Interior/U.S. Geological Survey) [
17
]; (
b
) collapse of a
lava dome generates pyroclastic flows on Sinabung volcano, Sumatra, Indonesia, in 2013 (photo credit:
Tom Pfeiffer/www.VolcanoDiscovery.com) [
18
]; (
c
) PDC generated by a lateral blast during the main
eruption of Mount Saint Helens, Washington State, USA, on 18 August 1980 (photo credit: Department
of the Interior/U.S. Geological Survey) [
19
]; (
d
) boiling over-based PDC generation mechanism from
an undersea volcano erupted off the coast of Tonga, on 18 March 2009 (photo credit: the image was
extracted from a video on YouTube website) [
20
]; (
e
) lava lake in Halema’uma’u crater, at the summit of
Kilauea volcano, on 26 May 2016 (photo credit: Department of the Interior/U.S. Geological Survey) [
22
];
and (
f
) lava and tephra fallout from the Eruption of Pu‘u ‘Ö‘ö, Kilauea, Hawaii, in 1984, tephra fallout
in this example is a combination of cinder, Pele’s tears, and Pele’s hair (photo credit: Department of the
Interior/U.S. Geological Survey) [23].
2.2. Nuclear Explosions
Typically, nuclear explosions including deep underground, shallow underwater, surface, and
atmospheric, form a mushroom-shaped cloud. In contrast, the high-altitude explosions form an
artificial aurora display with ionized region [
9
,
24
]. In this research, our focus will be on identifying
nuclear explosions which form a mushroom-shaped cloud.
The formation of a nuclear mushroom-shaped cloud can be described through an example of a
shallow underwater explosion, the Baker test, which was conducted at Bikini Lagoon in 1946. At the
beginning of the explosion, the water near the explosion was illuminated by the fireball formation.
However, the water waves caused distortion on the surface of the lagoon that prevented a clear view
of the fireball. The hot gas bubble underwater initiates a shock wave. Intersection of the shock wave
with the surface produces a slick, which is a ring of darkened water that is rapidly expanding, while
the reflection of the water shock wave at the surface causes a crack. A crack is a white circular patch
behind the dark region. Then, a spray dome (a column of broken water and spray) is thrown up over
the point of burst. The sides of the spray dome become steeper when the water rises. The disturbance
created by the underwater burst causes a series of waves to move outward from the center of the
explosion across the surface of the lagoon, where the test was conducted. The water flowed into the
cavity as the pressure of the bubble was released, which caused the water to be thrown up as a hollow
cylinder, or chimney of spray, called the “column/plume”. The radioactive contents of the bubble
were vented through the hollow column and formed a cauliflower-shaped cloud at the top in a shallow
underwater explosion, which concealed part of the upper portion of the column. It contained some of
the fission products, weapon residues, and a large amount of water in droplet form. Figure 2depicts
the mushroom-shaped cloud of the underwater Baker nuclear explosion [25].

J. Imaging 2017,3, 14 4 of 18
J. Imaging 2017, 3, 14 4 of 18
Figure 2. Mushroom-shaped cloud of the underwater Baker nuclear explosion in 1946 (photo courtesy
of National Nuclear Security Administration/Nevada Field Office) [26].
2.3. Research Hypothesis
Explosions phenomena under consideration can be characterized from the point of view of
image processing as follows:
(1) Pyroclastic Density Currents (PDCs) patterns have color properties that can be white (e.g.,
Figure 1a), or brown/brownish (e.g., Figure 1b), or dark color ranging from gray to black shades
(e.g., Figures 1c,d), have dense cloud shapes, and have multiple manifestation (shapes)
including: vertical column, laterally spread, avalanches which are generated by lava dome and
moving downslope of the volcano, and some volcanic eruptions can produce natural mushroom
clouds under the force of gravity.
(2) Lava fountains patterns have a luminous region of the image, and the color of the luminous
region depends on the temperature of the lava during the eruption. Therefore, lava may glow
golden yellow (~1090 °C), orange (~900 °C), bright cherry red (~700 °C), dull red (~600 °C),
or lowest visible red (~475 °C) [21].
(3) Lava and tephra fallout patterns have a luminous region (lava) and non-luminous region of the
image (tephra), and the color of lava is based on its temperature as explained in point 2. In
addition, the color of tephra including blocks, bombs, cinder/scoria/lapilli, Pele’s tears, and
Pele’s hair, coarse ash, and fine ash, etc. will be variable (light or dark colors) based on the type
of pyroclastic materials that are being ejected during the eruption.
(4) Nuclear explosions patterns have five properties. First, the color property where the initial color
of the mushroom cloud of a nuclear expulsion is red/reddish. When the fireball cools, water
condensation leads to the white color characteristic of the explosion cloud [9] and, secondly,
growth of the nuclear mushroom-shaped cloud, where it keeps rising until it reaches its
maximum height. Third, the shape which can be either mushroom-shaped cloud (our focus in
this research), or artificial aurora display with ionized region in case of space explosions).
Fourth, the luminous region of the image at which a luminous fireball can be viewed as flash or
light from hundreds of miles away for about 10 s, and then it is no longer luminous. Thus, the
non-luminous growing cloud appears for approximately 1–14 min, and fifth, the orientation
where the mushroom-shaped cloud has a single orientation.
In this research, we contribute the design and implementation of a novel vision-based
surveillance system for explosion phenomena using pattern recognition techniques including feature
Figure 2.
Mushroom-shaped cloud of the underwater Baker nuclear explosion in 1946 (photo courtesy
of National Nuclear Security Administration/Nevada Field Office) [26].
2.3. Research Hypothesis
Explosions phenomena under consideration can be characterized from the point of view of image
processing as follows:
(1)
Pyroclastic Density Currents (PDCs) patterns have color properties that can be white (e.g.,
Figure 1a), or brown/brownish (e.g., Figure 1b), or dark color ranging from gray to black shades
(e.g., Figure 1c,d), have dense cloud shapes, and have multiple manifestation (shapes) including:
vertical column, laterally spread, avalanches which are generated by lava dome and moving
downslope of the volcano, and some volcanic eruptions can produce natural mushroom clouds
under the force of gravity.
(2)
Lava fountains patterns have a luminous region of the image, and the color of the luminous
region depends on the temperature of the lava during the eruption. Therefore, lava may glow
golden yellow (
∼
1090
◦
C), orange (
∼
900
◦
C), bright cherry red (
∼
700
◦
C), dull red (
∼
600
◦
C), or
lowest visible red (∼475 ◦C) [21].
(3)
Lava and tephra fallout patterns have a luminous region (lava) and non-luminous region of
the image (tephra), and the color of lava is based on its temperature as explained in point 2. In
addition, the color of tephra including blocks, bombs, cinder/scoria/lapilli, Pele’s tears, and
Pele’s hair, coarse ash, and fine ash, etc. will be variable (light or dark colors) based on the type
of pyroclastic materials that are being ejected during the eruption.
(4)
Nuclear explosions patterns have five properties. First, the color property where the initial color
of the mushroom cloud of a nuclear expulsion is red/reddish. When the fireball cools, water
condensation leads to the white color characteristic of the explosion cloud [
9
] and, secondly,
growth of the nuclear mushroom-shaped cloud, where it keeps rising until it reaches its maximum
height. Third, the shape which can be either mushroom-shaped cloud (our focus in this research),
or artificial aurora display with ionized region in case of space explosions). Fourth, the luminous
region of the image at which a luminous fireball can be viewed as flash or light from hundreds of
miles away for about 10 s, and then it is no longer luminous. Thus, the non-luminous growing
cloud appears for approximately 1–14 min, and fifth, the orientation where the mushroom-shaped
cloud has a single orientation.

J. Imaging 2017,3, 14 5 of 18
In this research, we contribute the design and implementation of a novel vision-based surveillance
system for explosion phenomena using pattern recognition techniques including feature extraction
approaches and a classification technique. Hence, supervised learning is used to map the input frames
to the desired outputs and divide the space into regions or categories.
Consequently, we define seven patterns for some explosion and non-explosion phenomena
including: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions,
wildfires, fireworks, and, sky clouds. Towards the classification goal, we collected a new dataset
of 5327 2D RGB images, which are used for training the classifier. Since we do not have access to
explosion zones for testing the classifier and showing that the system can be applied in reality, we
evaluated the proposed research methodology on video sequences that were downloaded from the
YouTube website. These videos were taken in real outdoor environments for the seven scenarios of the
respective defined classes.
The objectives of employing feature extraction approaches and a classification technique in the
proposed framework are twofold. First, compute features which have the most relevant information
that characterize explosion phenomena from the input image data, which will result in reducing the
computational cost. This factor is often considered as the challenge to perform the desired classification
task of any application. Second, employ a classifier to categorize those phenomena and evaluate the
performance of the developed system.
In order to achieve high reliability of the explosion detection and classification system, we
propose developing a system that has the following characteristics: first, an explosion event is
represented by different feature sets or classes. These discriminative features obtained using our
proposed research methodology are invariant in terms of translation, illumination, rotation, and
scale. Second, it is processed by multiple types of analysis such as texture analysis, spatial (spectral)
analysis, and frequency analysis. Third, it provides different views or interpretations for the same
scene of an explosion or non-explosion phenomena under consideration in this research, and lastly,
combining texture, amplitude and frequency features provides a valuable insight into the images
under consideration.
As important as it is in practice, installation of such a system for detecting and categorizing
explosion phenomena at early stages would be valuable, and would play a significant role by providing
rapid emergency alerts not only to notify people about threats to their safety once an unexpected
explosion event occurred, but also to help community organizations and directors provide assessment
for civilians and explosion victims in a minimal time.
3. Related Work
Several projects are being conducted by U.S. Geological Survey (USGS) in the United States
on monitoring volcanic eruptions [
27
–
30
]. As seen in Figure 3, monitoring of volcanic eruptions
is accomplished using some important techniques as follows: (1) detecting volcanic tremor
(harmonic tremor); (2) hydrologic monitoring; (3) gas emission; (4) temperature measurements using
thermocouples, Thermal Infrared Radiation (TIR) video cameras, and infrared satellite sensors);
(5) satellite remote sensing such as Moderate Resolution Imaging Spectroradiometer (MODIS);
and, lastly, (6) monitoring volcano ground deformation by using: Electronic Distance Meter
(EDM), Tiltmeter, Global Positioning System (GPS), and Interferometric Synthetic Aperture Radar
(InSAR) images.
In addition, Langer et al. applied SVM and neural network to classify volcanic tremor (seismic
waves) patterns [
31
]. Furthermore, Iyer et al. proposed a classification system based on extracting
unique cepstral features from a volcano’s infrasonic signature, and feeding these features to the Radial
Basis Function Neural Network [
32
]. Moreover, Picchiani et al. used a single layer neural network to
classify ash clouds of MODIS images in the Thermal InfraRed (TIR) spectral [
33
]. Furthermore, Tan et
al. proposed a Bayesian detection algorithm and a near-optimal sensor selection algorithm to detect
earthquake events and timing using wireless sensor networks [34].

J. Imaging 2017,3, 14 6 of 18
J. Imaging 2017, 3, 14 6 of 18
Figure 3. Volcanic monitoring techniques which are employed by the USGS Volcano Hazards
Program (photo credit: Department of the Interior/U.S. Geological Survey) [28].
On the other hand, Dickinson and Tamarkin stated that there are various existing detection
techniques for nuclear explosions conducting in the air (atmospheric) and in the space (high altitude)
including: (1) acoustic; (2) debris sampling; (3) radio flash or electromagnetic pulse (EMP);
(4) satellites which use instruments to measure radiation from a nuclear detonation, such as X-rays,
gamma-rays, neutrons); (5) atmospheric fluorescence; (6) radio techniques which include very low
frequency (VLF), low frequency (LF), high frequency (HF), radio sounders, cosmic noise (Riometer);
(7) magnetic telluric; and (8) sunlight resonance scatter from debris [35]. In addition, USArray, a
component of EarthScope, is a program to deploy a dense network of seismographic stations across
United States. USArray consists of 400 broadband stations in a Transportable Array (TA) in a grid of
locations with roughly 70 spacing. Installation of TA is useful as it will lead to acquiring and
analyzing data about abnormal seismic events, which may be produced by underground nuclear
explosion on nearby areas [36].
In this paper, we are the first to address a multi-class classification for explosion phenomena as
it is still an unsolved problem in the field of pattern recognition on 2D RGB images. We propose a
novel framework design for categorizing explosion phenomena. The novelty of this framework
depends on the following factors: defining seven patterns, representing explosions phenomena using
multiple discriminative descriptors including texture features, amplitude features using YCCcolor
model, and frequency features using Radix-2 FFT, calculating the most significant 100 eigenvectors
and eigenvalues for each feature class, combining these features in an input vector, and, lastly,
employing a multi-class polynomial kernel SVM of degree 3 to maximize the margin between the
data points and the decision boundary for a proper classification.
4. Dataset
We define seven pattern classes for which each class has patterns that share common properties.
Explosion phenomena include the following classes (pyroclastic density currents (PDC), lava
fountains (LF), lava and tephra fallout (LT), nuclear mushroom clouds (NC), while non-explosion
phenomena include (wildfires (WF), fireworks (F), and, sky clouds (SC)). The classification criteria
for volcanic eruptions is the eruptive style that involves material ejected during the eruption. In
contrast, we identify the nuclear explosions according to the typical mushroom cloud. Thus, we are
not classifying space explosions. Table 1 illustrates the number of images in each category of
explosion and non-explosion phenomena that are used to design the classifier in the learning phase.
The totality of images in our training dataset is 5327.
Figure 3.
Volcanic monitoring techniques which are employed by the USGS Volcano Hazards Program
(photo credit: Department of the Interior/U.S. Geological Survey) [28].
On the other hand, Dickinson and Tamarkin stated that there are various existing detection
techniques for nuclear explosions conducting in the air (atmospheric) and in the space (high altitude)
including: (1) acoustic; (2) debris sampling; (3) radio flash or electromagnetic pulse (EMP); (4) satellites
which use instruments to measure radiation from a nuclear detonation, such as X-rays, gamma-rays,
neutrons); (5) atmospheric fluorescence; (6) radio techniques which include very low frequency (VLF),
low frequency (LF), high frequency (HF), radio sounders, cosmic noise (Riometer); (7) magnetic telluric;
and (8) sunlight resonance scatter from debris [
35
]. In addition, USArray, a component of EarthScope,
is a program to deploy a dense network of seismographic stations across United States. USArray
consists of 400 broadband stations in a Transportable Array (TA) in a grid of locations with roughly
70 spacing. Installation of TA is useful as it will lead to acquiring and analyzing data about abnormal
seismic events, which may be produced by underground nuclear explosion on nearby areas [36].
In this paper, we are the first to address a multi-class classification for explosion phenomena
as it is still an unsolved problem in the field of pattern recognition on 2D RGB images. We propose
a novel framework design for categorizing explosion phenomena. The novelty of this framework
depends on the following factors: defining seven patterns, representing explosions phenomena using
multiple discriminative descriptors including texture features, amplitude features using YCbCrcolor
model, and frequency features using Radix-2 FFT, calculating the most significant 100 eigenvectors and
eigenvalues for each feature class, combining these features in an input vector, and, lastly, employing a
multi-class polynomial kernel SVM of degree 3 to maximize the margin between the data points and
the decision boundary for a proper classification.
4. Dataset
We define seven pattern classes for which each class has patterns that share common properties.
Explosion phenomena include the following classes (pyroclastic density currents (PDC), lava fountains
(LF), lava and tephra fallout (LT), nuclear mushroom clouds (NC), while non-explosion phenomena
include (wildfires (WF), fireworks (F), and, sky clouds (SC)). The classification criteria for volcanic
eruptions is the eruptive style that involves material ejected during the eruption. In contrast,
we identify the nuclear explosions according to the typical mushroom cloud. Thus, we are not
classifying space explosions. Table 1illustrates the number of images in each category of explosion
and non-explosion phenomena that are used to design the classifier in the learning phase. The totality
of images in our training dataset is 5327.

J. Imaging 2017,3, 14 7 of 18
Table 1. Categories of our dataset.
Category Number of Images
Explosion
Pyroclastic density currents (PDC)
1522
Lava fountains (LF) 966
Lava and tephra fallout (LT) 346
Nuclear mushroom clouds (NC) 394
Non-explosion
Wildfires (WF) 625
Fireworks (F) 980
Sky clouds (SC) 494
Total 5327
Towards the classification goal, there was a need for a large dataset. However, there is a lack of
public databases on explosion phenomena under consideration in this study. Therefore, we had to
collect our own dataset. Furthermore, explosion or non-explosion areas in each image were cropped
manually, and stored using JPEG file format. Figure 4depicts samples of our datasets. Consequently,
images of the dataset that were used to train the classifier were downloaded from different resources
during 2014–2016. Some of these resources are as follows: USGS website [
37
], National Oceanic
and Atmospheric Administration (NOAA/NGDC) website [
38
], blogs [
39
], Volcano Adventures
website [
40
], Exploratorium [
41
], Trinity Atomic website [
24
], and Wikimedia Commons [
42
]. On the
other hand, multiple videos were downloaded from the YouTube website for testing phase [
43
–
47
].
The number of retrieved frames of video sequences for each category was 140 frames. Hence, the totality
of our testing set is 980 samples.
J. Imaging 2017, 3, 14 7 of 18
Table 1. Categories of our dataset.
Category Number of
Images
Explosion
Pyroclastic density currents (PDC) 1522
Lava fountains (LF) 966
Lava and tephra fallout (LT) 346
Nuclear mushroom clouds (NC) 394
Non-explosion
Wildfires (WF) 625
Fireworks (F) 980
Sky clouds (SC) 494
Total 5327
Towards the classification goal, there was a need for a large dataset. However, there is a lack of
public databases on explosion phenomena under consideration in this study. Therefore, we had to
collect our own dataset. Furthermore, explosion or non-explosion areas in each image were cropped
manually, and stored using JPEG file format. Figure 4 depicts samples of our datasets. Consequently,
images of the dataset that were used to train the classifier were downloaded from different resources
during 2014–2016. Some of these resources are as follows: USGS website [37], National Oceanic and
Atmospheric Administration (NOAA/NGDC) website [38], blogs [39], Volcano Adventures
website [40], Exploratorium [41], Trinity Atomic website [24], and Wikimedia Commons [42]. On the
other hand, multiple videos were downloaded from the YouTube website for testing phase [43–47].
The number of retrieved frames of video sequences for each category was 140 frames. Hence, the
totality of our testing set is 980 samples.
Figure 4. (a) pyroclastic density current (photo credit: Department of the Interior/U.S. Geological
Survey) [48]; (b) lava fountain (photo credit: Joschenbacher, Wikimedia Commons) [49];
(c) lava and tephra fallout (photo credit: Tom Pfeiffer/www.VolcanoDiscovery.com) [50];
(d) nuclear mushroom cloud (photo credit: Trinity atomic website) [24]; (e) wildfire (photo credit: John
Newman, Wikimedia Commons) [51]; (f) fireworks (photo credit: Ron Hipschman, Exploratorium,
www.exploratorium.edu) [41]; (g) sky cloud (photo credit: National Weather Service, National Oceanic
and Atmospheric Administration) [38].
Figure 4.
(
a
) pyroclastic density current (photo credit: Department of the Interior/U.S. Geological
Survey) [
48
]; (
b
) lava fountain (photo credit: Joschenbacher, Wikimedia Commons) [
49
]; (
c
) lava and
tephra fallout (photo credit: Tom Pfeiffer/www.VolcanoDiscovery.com) [
50
]; (
d
) nuclear mushroom
cloud (photo credit: Trinity atomic website) [
24
]; (
e
) wildfire (photo credit: John Newman, Wikimedia
Commons) [
51
]; (
f
) fireworks (photo credit: Ron Hipschman, Exploratorium, www.exploratorium.
edu) [
41
]; (
g
) sky cloud (photo credit: National Weather Service, National Oceanic and Atmospheric
Administration) [38].
5. Proposed Research Methodology
The proposed detection and classification system operates in four steps: gathering observations,
preprocessing, training (learning), and testing (classification). The following factors were taken into

J. Imaging 2017,3, 14 8 of 18
consideration when the system was designed and implemented: (1) selection of the transducer;
(2) definition of pattern classes; (3) pattern representation; (4) feature extraction methodologies;
(5) classifier design and learning; and (6) selection of training and test samples.
5.1. Design of the Proposed Framework
5.1.1. Preprocessing
During the preprocessing step, 2D RGB images are resized to 64
×
64 pixels. Moreover, in order
to calculate PCA feature class and Radix-2 FFT feature, each image is converted to a gray scale vector
of 8-bit intensity values.
5.1.2. Feature Extraction
A.
Principal Component Analysis
The Principal Component Analysis (PCA) algorithm is a mathematical approach that projects the
high-dimensional data onto a low-dimensional space. The reason for reducing the dimensions is that
we can focus on those dimensions where there is a high difference between images in our dataset (i.e.,
high variance). Hence, in order to represent each sample using the most significant 100 basis vectors,
the PCA algorithm is employed, and its steps during training (learning) are as follows [52]:
(1)
Obtain images for training phases
I1
,
I2· · · IM
, where
M=
5327, and represent each image
Ii
as
a vector Γi.
(2)
Find the average vector.
(3)
Find the mean adjusted vector for every image vector
Γi
, by subtracting the average vector from
each sample, and then assemble all data samples in a mean adjusted matrix.
(4)
Compute the covariance matrix C.
(5)
Calculate the eigenvectors
(V)
and eigenvalues
(λ
) of the computed covariance matrix C. After
computing the eigenvalues, we will sort the eigenvalues
(λ
) by magnitude, and we will only keep
the highest 100 eigenvalues and discard the rest.
(6)
Compute the basis vectors. Thus, from the previous step, we have 100 eigenvectors
(EV0,· · · ,EV99)
. These vectors will be assembled into an eigenvector matrix (EV). Then, we will
multiply EV by the mean adjusted matrix computed in step 3 to form the basis vectors.
(7)
Describe each sample using a linear combination of basis vectors.
Basis vectors have a magnitude of 1, and they are mutually orthogonal, meaning that the inner
product of any two vectors will produce zero. This also can be described as there is 90
◦
angle between
any two vectors in the eigenspace. These 100 features are invariant in terms of translation, illumination,
and scale.
During the testing phase, each test image is resized to 64
×
64 pixels and then converted to a
vector of size
(4096 ×1)
pixels. After that, the average image from the training process is subtracted
from each test sample, resulting in the mean adjusted image. Consequently, the mean adjusted image
of each sample gets projected on the eigenspace of 100 significant eigenvectors. Figure 5summarizes
steps of the PCA algorithm during training (learning).

J. Imaging 2017,3, 14 9 of 18
J. Imaging 2017, 3, 14 9 of 18
Figure 5. Steps of the PCA algorithm during training (learning).
B. Features in the spatial domain: amplitude features
Color is one of the physical properties of explosion and non-explosion phenomena, as it depends
on the temperature and the composition of each phenomenon of the proposed application.
Amplitude (spectral) features in the proposed framework are extracted by linear transformation
of bitmap pixel component intensities (RGB) toYCC color space [53]. YCC represents color as
luminance componentY, and chrominance components C, which is blue minus luma (B − Y), and
C,which is red minus luma (R − Y).
The choice of the appropriate color model depends on the target application and the
effectiveness of the color transformation algorithm. Calculating YCCof an image is more efficient
than RGB because the human eye is more sensitive to change in brightness than change in color. 
color space can be described as an illumination dependent color model. To overcome this
disadvantage of RGB, YCC is used [54].
We implemented the following formula that describes the conversion of RGB color space into
YCC color space according to ITU-R BT.601 [53]:

Y
C
C=16
128
128+0.257 0.504 0.098
−0.148 −0.291 0.439
0.439 −0.368 −0.071∙R
G
B, (1)
where 8-bit representation of each component of YCC is specified by the recommendation 601, and
R,G,B∈󰇟0,255󰇠,Y ∈󰇟16,235󰇠, and CC ∈󰇟16,240󰇠. Y has an excursion of 219 and an offset
of +16, placing black at code 16 and white at code 235, while CandC have excursions of ±112
and offset of +128.
After we represent 5327samples of the training set using the YCCcolor model, the dimension
of the 2D training matrix will be (5327×12288). Then, we apply PCA to extract the 100 most
significant eigenvectors and eigenvalues. The 2D components matrix for the training phase is of
dimensions (5327×100), whereas the components matrix for testing 140 samples is of dimensions
(140×100).As seen in Figure 6, the block diagram shows the proposed YCC algorithm in the
time domain.
Figure 6. Block diagram for extracting the highest 100 eigenvectors after employing time domain
YCC encoding schema.
C. Features in the transform domain
In the proposed framework, features in the transform domain are calculated using the Radix-2
FFT algorithm. Radix-2 is a mathematical mechanism used to convert a spatial-domain image
representation into a frequency-domain representation. It decomposes the image into a weighted
sum of complex exponential functions called spectral components. The weighted terms at each
Figure 5. Steps of the PCA algorithm during training (learning).
B. Features in the spatial domain: YCbCramplitude features
Color is one of the physical properties of explosion and non-explosion phenomena, as it depends
on the temperature and the composition of each phenomenon of the proposed application.
Amplitude (spectral) features in the proposed framework are extracted by linear transformation
of bitmap pixel component intensities
(RGB)
to
YCbCr
color space [
53
].
YCbCr
represents color as
luminance component
Y
, and chrominance components
Cb
, which is blue minus luma (B
−
Y), and
Cr
,
which is red minus luma (R−Y).
The choice of the appropriate color model depends on the target application and the effectiveness
of the color transformation algorithm. Calculating
YCbCr
of an image is more efficient than
RGB
because the human eye is more sensitive to change in brightness than change in color.
RGB
color space
can be described as an illumination dependent color model. To overcome this disadvantage of
RGB
,
YCbCris used [54].
We implemented the following formula that describes the conversion of
RGB
color space into
YCbCrcolor space according to ITU-R BT.601 [53]:



Y
Cb
Cr


=


16
128
128


+


0.257 0.504 0.098
−0.148 −0.291 0.439
0.439 −0.368 −0.071



·


R
G
B


, (1)
where 8-bit representation of each component of
YCbCr
is specified by the recommendation 601, and
R
,
G
,
B∈[0, 255]
,
Y∈[16, 235]
, and
Cband Cr∈[16, 240]
.
Y
has an excursion of 219 and an offset
of
+
16, placing black at code 16 and white at code 235, while
Cband Cr
have excursions of
±
112 and
offset of +128.
After we represent 5327 samples of the training set using the
YCbCr
color model, the dimension of
the 2D training matrix will be
(5327 ×12288)
. Then, we apply PCA to extract the 100 most significant
eigenvectors and eigenvalues. The 2D components matrix for the training phase is of dimensions
(5327 ×100)
, whereas the components matrix for testing 140 samples is of dimensions
(140 ×100)
. As
seen in Figure 6, the block diagram shows the proposed YCbCralgorithm in the time domain.
J. Imaging 2017, 3, 14 9 of 18
Figure 5. Steps of the PCA algorithm during training (learning).
B. Features in the spatial domain: amplitude features
Color is one of the physical properties of explosion and non-explosion phenomena, as it depends
on the temperature and the composition of each phenomenon of the proposed application.
Amplitude (spectral) features in the proposed framework are extracted by linear transformation
of bitmap pixel component intensities (RGB) toYCC color space [53]. YCC represents color as
luminance componentY, and chrominance components C, which is blue minus luma (B − Y), and
C,which is red minus luma (R − Y).
The choice of the appropriate color model depends on the target application and the
effectiveness of the color transformation algorithm. Calculating YCCof an image is more efficient
than RGB because the human eye is more sensitive to change in brightness than change in color. 
color space can be described as an illumination dependent color model. To overcome this
disadvantage of RGB, YCC is used [54].
We implemented the following formula that describes the conversion of RGB color space into
YCC color space according to ITU-R BT.601 [53]:

Y
C
C=16
128
128+0.257 0.504 0.098
−0.148 −0.291 0.439
0.439 −0.368 −0.071∙R
G
B, (1)
where 8-bit representation of each component of YCC is specified by the recommendation 601, and
R,G,B∈󰇟0,255󰇠,Y ∈󰇟16,235󰇠, and CC ∈󰇟16,240󰇠. Y has an excursion of 219 and an offset
of +16, placing black at code 16 and white at code 235, while CandC have excursions of ±112
and offset of +128.
After we represent 5327samples of the training set using the YCCcolor model, the dimension
of the 2D training matrix will be (5327×12288). Then, we apply PCA to extract the 100 most
significant eigenvectors and eigenvalues. The 2D components matrix for the training phase is of
dimensions (5327×100), whereas the components matrix for testing 140 samples is of dimensions
(140×100).As seen in Figure 6, the block diagram shows the proposed YCC algorithm in the
time domain.
Figure 6. Block diagram for extracting the highest 100 eigenvectors after employing time domain
YCC encoding schema.
C. Features in the transform domain
In the proposed framework, features in the transform domain are calculated using the Radix-2
FFT algorithm. Radix-2 is a mathematical mechanism used to convert a spatial-domain image
representation into a frequency-domain representation. It decomposes the image into a weighted
sum of complex exponential functions called spectral components. The weighted terms at each
Figure 6.
Block diagram for extracting the highest 100 eigenvectors after employing time domain
YCbCrencoding schema.
C.
Features in the transform domain
In the proposed framework, features in the transform domain are calculated using the Radix-2
FFT algorithm. Radix-2 is a mathematical mechanism used to convert a spatial-domain image

J. Imaging 2017,3, 14 10 of 18
representation into a frequency-domain representation. It decomposes the image into a weighted sum
of complex exponential functions called spectral components. The weighted terms at each frequency
are the complex amplitude and phase. The discrete definition of Radix-2 FFT is given as follows [55]:
Xk=
N/2−1
∑
m=0
x2me−2πi
N(2m)k+
N/2−1
∑
m=0
x2m+1e−2πi
N(2m+1)k. (2)
The Radix-2 FFT algorithm can be achieved on 2D images by employing the following
steps [55–57]:
(1)
Perform a time-domain decomposition using a bit-reversal sorting algorithm to transform
the input spatial image into a bit-reverse order array, and there are
log2N
stages needed for
this decomposition.
(2)
A two-dimensional FFT can be executed as two one-dimensional FFT in sequence where 1D FFT
is performed across all rows, replacing each row with its transform. Then, 1D FFT is performed
across all columns, replacing each column with its transform.
(3)
Combine the N frequency spectra in the correct reverse order at which the decomposition in the
time domain was achieved. This step involves calculation of the core computational module of
base-2-domain FFT algorithm, which is called a butterfly operation.
After converting 5327 samples of the training set from the spatial domain into the frequency
domain, the dimension of the 2D training matrix will be
(5327 ×4096)
. Then, we apply the PCA
algorithm to extract the 100 most significant eigenvectors and eigenvalues, hence removing the noise.
The components matrix for the training phase is of dimensions
(5327 ×100)
, and the dimensions of
the components matrix for testing 140 samples is
(140 ×100)
. Figure 7depicts a block diagram for
extracting the 100 highest eigenvectors after employing the Radix-2 FFT algorithm.
J. Imaging 2017, 3, 14 10 of 18
frequency are the complex amplitude and phase. The discrete definition of Radix-2 FFT is given as
follows [55]:
Χ=  
2


⁄
 (2)+

2


⁄
 (2+1). (2)
The Radix-2 FFT algorithm can be achieved on 2D images by employing the following
steps [55–57]:
(1) Perform a time-domain decomposition using a bit-reversal sorting algorithm to transform the
input spatial image into a bit-reverse order array, and there are log stages needed for this
decomposition.
(2) A two-dimensional FFT can be executed as two one-dimensional FFT in sequence where 1D FFT
is performed across all rows, replacing each row with its transform. Then, 1D FFT is performed
across all columns, replacing each column with its transform.
(3) Combine the N frequency spectra in the correct reverse order at which the decomposition in the
time domain was achieved. This step involves calculation of the core computational module of
base-2-domain FFT algorithm, which is called a butterfly operation.
After converting 5327samples of the training set from the spatial domain into the frequency
domain, the dimension of the 2D training matrix will be (5327×4096). Then, we apply the PCA
algorithm to extract the 100 most significant eigenvectors and eigenvalues, hence removing the noise.
The components matrix for the training phase is of dimensions (5327×100), and the dimensions of
the components matrix for testing 140 samples is (140×100). Figure 7 depicts a block diagram for
extracting the 100 highest eigenvectors after employing the Radix-2 FFT algorithm.
Figure 7. Block diagram for extracting the highest 100 eigenvectors after employing Radix-2
FFT algorithm.
Advantages of using Radix-2 FFT are as follows:
(1) Spectral analysis of the image using Radix-2 FFT reveals a significant amount of information
about the geometric structure of 2D spatial images due to the use of orthogonal basis functions.
Consequently, representing an image in the transform domain has a larger range than in the
spatial domain.
(2) An image can contain high-frequency components if its gray levels (intensity values) are
changing rapidly, or low-frequency components if its gray levels are changing slowly over the
image space. For detecting such a change, Radix-2 FFT can be efficiently applied.
5.1.3. One-against-One Multi-Class Support Vector Classification
Support Vector Machine (SVM) is a supervised classification technique, and it is basically a
binary classification method. It maximizes the margin between the data points and the decision
boundary. The functions that are used to project the data from the input space to the feature space
Figure 7.
Block diagram for extracting the highest 100 eigenvectors after employing Radix-2
FFT algorithm.
Advantages of using Radix-2 FFT are as follows:
(1)
Spectral analysis of the image using Radix-2 FFT reveals a significant amount of information
about the geometric structure of 2D spatial images due to the use of orthogonal basis functions.
Consequently, representing an image in the transform domain has a larger range than in the
spatial domain.
(2) An image can contain high-frequency components if its gray levels (intensity values) are changing
rapidly, or low-frequency components if its gray levels are changing slowly over the image space.
For detecting such a change, Radix-2 FFT can be efficiently applied.

J. Imaging 2017,3, 14 11 of 18
5.1.3. One-against-One Multi-Class Support Vector Classification
Support Vector Machine (SVM) is a supervised classification technique, and it is basically a binary
classification method. It maximizes the margin between the data points and the decision boundary.
The functions that are used to project the data from the input space to the feature space are called
kernels. Accordingly, after employing different SVM kernels, we find that polynomial kernel of degree
3 is the appropriate kernel for classifying multiple phenomena in our target application. Degree-3
polynomial kernel SVM is defined as follows:
KXi,Xj=γXT
iXj+rd,γ>0, (3)
where γ,r, and d= 3 are kernel parameters [58].
Even though the figure above describes a binary SVM classifier, multiple machines can be
constructed to deal with a multi-class case [
59
]. Consequently, one common strategy for SVM that can
handle a multi-class categorization for the proposed application is the one-against-one technique, which
constructs a machine for each pair of classes during the training process, resulting in
M=N(N−1)
2
machines, where
N=
7 classes, and
M=
21 machines. In addition, the kernel is polynomial of
degree 3, the number of training samples is 5327, and the length of the input vector is as same as
the number of extracted features (300 features), such that values of input vectors are normalized
between [–1,1].
Training SVM requires the solution of a very large quadratic programming (QP) optimization
problem. The QP problem is solved by using the Sequential Minimal Optimization (SMO) learning
algorithm [60].
For the testing phase, the number of testing samples in each experiment is 140, and the length of
the input vector is 300. The classification of test samples is done according to the maximum voting,
at which each SVM votes for one class. Consequently, a sample
x
is classified as it belongs to class
i∗
whose decision function fi∗produces the largest value. The formula is given as follows:
i∗=arg max
i=1,...,Mfi(x)=arg max
i=1,...,MwT
iϕ(x)+bi. (4)
6. Experimental Results and Discussion
The classification system was implemented using C# language under the Microsoft Net
framework 4.6 (Microsoft Corporation, New York, NY, USA). It was also operating on a workstation
with an Intel(R) Core(TM) i7 CPU at 3.20 GHz, RAM (20.0 GB), and 64-bit OS (Dell Inc., Round Rock,
TX, USA).
The classification system operates in two phases: training and testing. As seen in Table 1,
the dataset of 5327 samples was used for training the multi-class degree-3 polynomial kernel SVM
classifier. For testing phase of the classification system, we extracted seven videos from multiple
videos, which are available on YouTube, as stated in Section 4. The extracted videos were saved using
MPEG file format, and their resolution was 720
×
480 pixels at 29 frames per second (fps). These videos
were converted to frames and saved using JPEG file format. Since the length of the extracted videos
is varied, the max number of test samples for each category was determined based on the length of
the shortest video among them for consistency. Accordingly, the first 140 frames of each video were
used for testing the proposed classification methodology. Theses frames were resized to 64
×
64 pixels.
Figure 8displays some of the retrieved video sequences for the seven categories under consideration.

J. Imaging 2017,3, 14 12 of 18
J. Imaging 2017, 3, 14 12 of 18
Figure 8. Some samples of retrieved video sequences in the testing set.
During the testing phase of the classification system, each frame was defined by 100 features
after extracting texture features using the PCA algorithm, 100 features after applying YCC+ PCA,
and, lastly, 100 features after applying Radix-2 FFT + PCA. Furthermore, these 300 features were
combined into one input vector, and then passed to a degree-3 polynomial kernel SVM classifier and
assigned to a specific category. Accuracy of the classification system was computed using the
following formula:
Accurac
y
=+
+++×100%, (5)
where , , , and  are the number of true positive, true negative, false positive, and false
negative cases, respectively. Table 2 illustrates details of videos of the seven classes under
consideration as well as a comparison between patterns of the proposed classification system in terms
of accuracy (classification rate).
Figure 8. Some samples of retrieved video sequences in the testing set.
During the testing phase of the classification system, each frame was defined by 100 features after
extracting texture features using the PCA algorithm, 100 features after applying
YCbCr
+ PCA, and,
lastly, 100 features after applying Radix-2 FFT + PCA. Furthermore, these 300 features were combined
into one input vector, and then passed to a degree-3 polynomial kernel SVM classifier and assigned to
a specific category. Accuracy of the classification system was computed using the following formula:
Accuracy =TP +T N
TP +FP +TN +F N ×100%, (5)
where
TP
,
TN
,
FP
, and
FN
are the number of true positive, true negative, false positive, and false
negative cases, respectively. Table 2illustrates details of videos of the seven classes under consideration
as well as a comparison between patterns of the proposed classification system in terms of accuracy
(classification rate).

J. Imaging 2017,3, 14 13 of 18
Table 2. Comparison between patterns in terms of accuracy.
Category Frame Rate
Resolution of
Video
Sequences
Number of
Retrieved Frames
for Testing
Frames Resized
During
Preprocessing
Features
Input Vector Accuracy
Video 1—PDC 29 fps 720 ×480 140 64 ×64 300 98.57%
Video 2—LF 29 fps 720 ×480 140 64 ×64 300 90.71%
Video 3—LT 29 fps 720 ×480 140 64 ×64 300 83.57%
Video 4—NC 29 fps 720 ×480 140 64 ×64 300 100%
Video 5—WF 29 fps 720 ×480 140 64 ×64 300 85.71%
Video 6—F 29 fps 720 ×480 140 64 ×64 300 100%
Video 7—SC 29 fps 720 ×480 140 64 ×64 300 100%
The proposed explosion categorization system achieved 94.08% accuracy, where NC, F, and
SC samples were classified correctly with 100% accuracy, followed by PDC, LF, WF, and LT, which
achieved 98.57%, 90.71%, 85.71%, and 83.57%, respectively. Hence, results indicate that the design of
the proposed system is effective. However, LT and WF phenomena are complex.
Figure 9depicts a chart of classified versus misclassified input test sets of 140 samples for each
category of the proposed application. Out of 140 frames of each testing set, 2, 13, 23, and 20, were
misclassified for PDC, LF, LT, and WF, respectively. In contrast, all frames of NC, F, and SC testing sets
were classified correctly.
J. Imaging 2017, 3, 14 13 of 18
Table 2. Comparison between patterns in terms of accuracy.
Category Frame
Rate
Resolution
of Video
Sequences
Number of
Retrieved
Frames for
Testing
Frames
Resized
During
Preprocessing
Features
Input
Vector
Accuracy
Video 1—PDC 29 fps 720 × 480 140 64 × 64 300 98.57%
Video 2—LF 29 fps 720 × 480 140 64 × 64 300 90.71%
Video 3—LT 29 fps 720 × 480 140 64 × 64 300 83.57%
Video 4—NC 29 fps 720 × 480 140 64 × 64 300 100%
Video 5—WF 29 fps 720 × 480 140 64 × 64 300 85.71%
Video 6—F 29 fps 720 × 480 140 64 × 64 300 100%
Video 7—SC 29 fps 720 × 480 140 64 × 64 300 100%
The proposed explosion categorization system achieved 94.08% accuracy, where NC, F, and SC
samples were classified correctly with 100% accuracy, followed by PDC, LF, WF, and LT, which
achieved 98.57%, 90.71%, 85.71%, and 83.57%, respectively. Hence, results indicate that the design of
the proposed system is effective. However, LT and WF phenomena are complex.
Figure 9 depicts a chart of classified versus misclassified input test sets of 140 samples for each
category of the proposed application. Out of 140 frames of each testing set, 2, 13, 23, and 20, were
misclassified for PDC, LF, LT, and WF, respectively. In contrast, all frames of NC, F, and SC testing
sets were classified correctly.
Figure 9. Chart of classified versus misclassified samples of the 980 testing set.
Table 3 demonstrates the confusion matrix for multi-class degree-3 polynomial kernel SVM
classifier where testing samples of 140 for each category are represented by 300 features.
Nevertheless, data of images are complex. In this view, the LT category is a combination of lava
and tephra fallout products, which deposited together directly by an effusive eruption in this
example. The display of some LT scenes results in misclassified samples among PDC and LF.
Likewise, due to the luminous region (lava) which is similar to the flame, and non-luminous region
of the image (tephra) which is similar to the dark smoke, some LT scenes were misclassified as WF.
Moreover, WF is also a complex category; the video we played displays a wildfire that occurred
during the day and another during the night. Wildfire produce flames (luminous region) during the
flaming stage of the combustion process, and smoke (flameless region) during smoldering
combustion (non-flaming stage of fire). Flames of wildfires typically glow red then orange and then
transmitting to yellow. Then, hot flaming combustion transfers more fuel (wood) into carbon
compounds that formed into tiny elements. These particles absorb light, which makes them appear
as gray to black smoke. On the other hand, smoldering combustion typically reflects light, producing
a white color appearance from the smoke. Thus, wildfire may generate white smoke. As a result,
some of the WF samples were classified as if they belong to PDC, LF, and F.
Figure 9. Chart of classified versus misclassified samples of the 980 testing set.
Table 3demonstrates the confusion matrix for multi-class degree-3 polynomial kernel SVM
classifier where testing samples of 140 for each category are represented by 300 features.
Nevertheless, data of images are complex. In this view, the LT category is a combination of lava
and tephra fallout products, which deposited together directly by an effusive eruption in this example.
The display of some LT scenes results in misclassified samples among PDC and LF. Likewise, due
to the luminous region (lava) which is similar to the flame, and non-luminous region of the image
(tephra) which is similar to the dark smoke, some LT scenes were misclassified as WF.
Moreover, WF is also a complex category; the video we played displays a wildfire that occurred
during the day and another during the night. Wildfire produce flames (luminous region) during the
flaming stage of the combustion process, and smoke (flameless region) during smoldering combustion
(non-flaming stage of fire). Flames of wildfires typically glow red then orange and then transmitting to
yellow. Then, hot flaming combustion transfers more fuel (wood) into carbon compounds that formed
into tiny elements. These particles absorb light, which makes them appear as gray to black smoke. On
the other hand, smoldering combustion typically reflects light, producing a white color appearance
from the smoke. Thus, wildfire may generate white smoke. As a result, some of the WF samples were
classified as if they belong to PDC, LF, and F.

J. Imaging 2017,3, 14 14 of 18
Table 3. Confusion matrix for multi-class degree-3 polynomial kernel SVM classifier.
Actual Predicted Results
PDC LF LT NC WF F SC
PDC (140) 138 0 2 0 0 0 0
LF (140) 0 127 0 0 0 13 0
LT (140) 10 8 117 0 5 0 0
NC (140) 0 0 0 140 0 0 0
WF (140) 5 9 0 0 120 6 0
F (140) 0 0 0 0 0 140 0
SC (140) 0 0 0 0 0 0 140
In addition, a few PDC samples were misclassified as LT because of the non-luminosity property
they both share. Furthermore, some lava samples were classified as if they belong to the fireworks
class due to its luminosity property that both categories share. In particular, the video displays a
lava fountain that was venting during the night. Luminosity of lava is related to its color (physical
property), which indicates the composition and temperature. On the other hand, light from the
fireworks is due to the chemical reactions of metal salts. In a consequence, once fireworks are ignited
by lighter or a match, the energy absorbed by an atom of a metal salt reorganizes its electrons from
their lowest-energy state, which is called the ground state, up to a higher-energy state that is called an
excited state. The excess energy of the excited state is released as a light that has a specific color to be
displayed as ignitable shapes.
One reason for the success of a multi-class degree-3 polynomial kernel SVM classifier as a
kernel-based method is that the kernel function takes relationships that are implicit in the data and
makes them explicit. Hence, the result being that the categorization of patterns takes place more
easily [61].
In addition, we analyze the time needed to classify one frame during the testing phase. Thus,
we divide the total time frame into three stages as follows: (1) the time consumed for extracting
300 features of one frame; (2) the time utilized to pass one frame to the SVM classifier; and (3) the
classification time that was required to assign the frame into a specific category. Table 4illustrates
corresponding details regarding testing time. As a result, the total execution time for testing one frame
is approximately 0.12 s.
Table 4. Time analysis in seconds for testing one frame.
Phase Time in Seconds
Time for extracting 300 features 0.073
Time to pass 1 frame to the classifier 0.001
Classification time 0.046
Total time ≈0.12
7. Conclusions
In this research, we addressed a new problem in the pattern recognition field, a vision-based
classification system for explosion phenomena associated with a new training dataset of 5327 images
for some of explosions and non-explosions phenomena including: pyroclastic density currents, lava
fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and, sky clouds.
Furthermore, we proposed employing the following approaches for feature extraction: texture,
amplitude and frequency features. Then, a PCA algorithm was employed to extract the 100 most
significant eigenvectors and eigenvalues for each feature class. Moreover, combining the measured
features in an input vector is essential because it provides multiple analysis, views, and interpretations
for explosion classification images. Hence, a valuable insight of explosion scenes is accomplished.

J. Imaging 2017,3, 14 15 of 18
Consequently, an input vector of 300 features is fed to a multi-class polynomial kernel SVM of degree 3.
This proposed research methodology was evaluated on 980 frames of video sequences for seven
scenarios of explosions and non-explosions phenomena, at which each category includes 140 frames
for testing. Results show that a high classification rate of 94.08% was achieved to classify 980 frames.
In addition, a reasonable execution time of 0.12 s was accomplished to categorize one frame during the
testing phase.
We conclude that installation of such a system will lead to mitigating explosion disasters’ impact
to the level that people can still survive, and minimize encounters.
For future work, we plan to employ a deep learning multi-layer neural network trained with a
back propagation algorithm, and a convolutional deep learning neural network in order to improve
the classification accuracy.
We also plan to implement a classification system based on extracting thermal descriptors from
thermal infrared images of the seven classes under consideration in this research.
Author Contributions:
This research paper is part of Sumaya Abusaleh’s Ph.D. dissertation. Ausif Mahmood
and Khaled Elleithy were the advisor and co-advisor, respectively. Sarosh Patel is a member of the dissertation
committee. Sumaya Abusaleh proposed the idea, collected the training dataset during 2014–2016, developed the
classification system, performed the experiments, analyzed the results, and wrote the paper. Extensive discussion
during the design and implementation of the proposed framework to determine the best approach for the
classification system, and evaluation matrices were done over the past year by Sumaya Abusaleh, Ausif Mahmood,
Khaled Elleithy, and Sarosh Patel.
Conflicts of Interest: The authors declare no conflict of interest.
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