Oil Spill Detection using Segmentation based Approaches
D. Mira
1
, P. Gil
2
, B. Alacid
1
and F. Torres
2
1
Computer Science Research Institute, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690,
San Vicente del Raspeig, Alicante, Spain
2
Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante,
Carretera San Vicente del Raspeig s/n, 03690, San Vicente del Raspeig, Alicante, Spain
{damian.mira, pablo.gil, bea.alacid, fernando.torres}@ua.es
Keywords: Oils Spill Detection, Remote Sensing, Segmentation, Slar Data.
Abstract: This paper presents a description and comparison of two segmentation methods for the oil spill detection in
the sea surface. SLAR sensors acquire video sequences from which snapshots are extracted for the detection
of oil spills. Both approaches are segmentation based on graph techniques and J-image respectively. Finally,
the aim of applying both approaches to SLAR snapshots, as shown, is to detect the largest part of the oil
slick and minimize the false detection of the spill.
1 INTRODUCTION
Year on year, the increase in traffic of goods and
people has resulted in the proliferation of cargo and
passenger ships. This has placed pressure on
maritime surveillance to deal quickly and effectively
with marine mishaps. Surveillance is necessary to
prevent bad practices that lead to water pollution,
such as the illegal tank cleaning of ships. The
maritime surveillance requires the use of
information from different types of sensors in
different locations. These sensors can be located on
satellites, as SAR (Synthetic Aperture Radar), or on
board, such as SLAR (Side Looking Airborne
Radar) and thermal sensors or transponders.
The biggest problem in marine pollution is oil
spills. Cases like the Prestige (García-Mira et. al.
2006) and the oil drilling dig of the Gulf of Mexico,
(Ramseur 2010), exemplify the problems of these
large-scale disasters. But it is not only the large-
scale disasters that have a damaging impact on eco-
systems. An example of a lesser magnitude spill is
the sinking of the ship Oleg Naydenov, 15 miles
south of Punta Maspalomas (Gran Canaria, Spain),
which presented a threat to the ecosystem and it was
also in a touristic zone.
The demand to control oil spills, has resulted in
numerous studies for the detection, monitoring and
controlling of such discharges on the sea surface.
SAR sensors are used to provide information in the
majority of studies which autonomously carry out
the task of detecting oil spills (Topouzelis, 2008).
The widespread use of these radars is due to their
attributes, such as invariance to different climatic
conditions, clouds, day/night and so forth. On the
contrary, these radars have some features which
perturb the detection of oil spills on the sea surface;
among them can be highlighted, the wind speed at
the surface (Brekke and Solberg, 2005), the presence
of accumulations of marine plankton that causes
false positives (Blondeau-Patissier et. al. 2014) or
the presence of layers of floating ice (Brekke et. al.
2014). SAR are mounted at satellites and they
present some disadvantages such as the satellite
must be in the proper orbit to scan a specific area
and, therefore, both the necessary response time and
the need for emergency action are hampered.
There is a scarcity of studies using SLAR
technology whose objective is oil spill detection.
Nevertheless, for detecting oil spills from the
information acquired by SAR radars, various
techniques have been documented, such as artificial
intelligence (Singha et.al. 2012), statistical and
mathematical techniques (Li and Li, 2010),
information extraction from features in image (Hu
and Xiao, 2013) among others. Therefore, in this
paper we present below some examples of solutions
based on segmentation processes using features in
image.
Thus, (Solberg et al. 2007) presented an oil spill
detection algorithm based on segmentation by
adaptive thresholding and Gaussian pyramids, and
442
Mira, D., Gil, P., Alacid, B. and Torres, F.
Oil Spill Detection using Segmentation based Approaches.
DOI: 10.5220/0006191504420447
In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), pages 442-447
ISBN: 978-989-758-222-6
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
extraction of features from the image for
classification. The shown success rate in that work
was between 72% and 77% accuracy in the detection
of oil slicks. Authors as (Mera et al., 2012) proposed
an algorithm with an adaptive thresholding from a
calibration of the images to represent in each pixel
the reflection backscatter of the radar, and the
estimation of the wind in the sea surface. The
aforementioned authors add to the previous work a
characterization algorithm oriented to the
classification of contour shapes of the regions
labelled as oil spill (Mera et al., 2014). Others
authors in (Chang et. al., 2008) showed a method
based on clustering and region segmentation. The
segmentation was done by a technique based on the
moment preservation. This method splits the image
in regions with similar moment. Later, the neighbour
regions are combined with the N-nearest-neighbour
rule by the spatial correlation data of each region. A
data model of the oil spill is built with the
segmentation results, which is approximated by the
use of normal distributions. Finally a Generalized
Likelihood Ratio Test (GLRT) is used to identify the
oil spills.
Another methodology used for the detection of
oil spills was that proposed (Shu et al., 2010) in
which the spatial density was used, (defined by the
quantity of pixels in an area with an intensity value),
which is selected as it is likely to indicate an oil
spill. To do this, initially a Gaussian smooth with a
3x3 mask and a standard deviation of 0.5 is done.
Then, a segmentation based on an intensity
thresholding by Otsu was performed. Subsequently,
a second segmentation process, in which the
threshold is the density of the pixels considered as a
spill, is carried out for the detection. Finally, a filter
was applied in order to avoid false positives by
determining the significant region pixels according
to their area and contrast.
2 AIRBONE SENSORY SYSTEM
The sensor used (SLAR) is an airborne radar, whose
technology is similar to the synthetic aperture radar,
SAR. Some differences between SAR and SLAR
exist in the identification of two scanning zones by
the SLAR: the blind zone of sensor, and the valid
area for data processing as shown in Figure 1. In
(Alacid and Gil, 2016), the authors proposed a
method to solve the problem of identifying the blind
zone of the sensor and other disturbances which
cause noise, as aircraft turns, using image processing
techniques without other information as altimeter,
inclinometer and so forth.
Figure 1: Diagram of scan areas of the SLAR.
3 SEGMENTATION OF OIL
SPILLS
The first approach of our work was based on testing
some well-known techniques of adaptive
thresholding similar to (Liu et. al., 2011) as well as
some saliency map algorithms as in (Jiang et. al.,
2013). It did not yield satisfactory results in terms of
correctly identify the region with oil spills.
Moreover, this last algorithm presents a high
computation cost and over-segmentation in the
region of the spot.
3.1 Previous Filter of the Image
In order to solve the problems presented in the
methods previously commented, two new
approaches have been implemented, one, graph-
based segmentation and another segmentation based
on J-image.
In both methods a pre-processing step is
performed, after the process of identification of the
blind zone sensor and turns of the aircraft (Alacid
and Gil, 2016). This pre-processing is performed to
remove noise in the area of the selected snapshot and
highlight areas which may potentially represent oil
spills (Figure 2a). For this, a Gaussian filter is used
and subsequently an analysis of the shape and values
of the histogram is done to perform an equalization.
This allows us to enhance the contrast in order to
eliminate the outliers of high and low intensity,
amounting to around 2%. Subsequently, a process is
performed to manually remove borders of certain
areas in the image that could be indicative of oil spill
regions, although in fact they do not represent oil
spills. Thus the pixels of these unrepresentative
areas are homogenized or otherwise, they are
reduced to make them less representative, resulting
Oil Spill Detection using Segmentation based Approaches
443
in a more accurate detection of other regions that
may contain oil spills (Figure 2b).
3.2 Gray Level Co-Occurrence Matrix
The proposed segmentation methods use co-
occurrence matrix to improve the image processing
and enhance the detection process. The Gray Level
Co-occurrence Matrix, (GLCM) is commonly used
to mathematically measure textures in the image
(Haralick, 1979). This matrix approximates the joint
probability distribution of a pair of pixels. Thus, it
describes the frequency by which a gray level is
displayed in a specific spatial relationship to another
gray value within the specified window.
Within the values that can be obtained through
GLCM, the values of energy (1), and correlation (2)
have been used.

,

,
(1)

,




,
(2)
where
,
is the probability of co-occurrence of gray
values for i, j, where i is the position in the row and j
the position on the column. N represents the size of
the window, μ the mean for i and j, and σ variance
for i and j.
The result of applying these values to the pre-
processed image can be seen in Figure 2c-d.
3.3 Graph based Segmentation
Given the need for a robust segmentation in which
the non-homogeneity is taken into account for
proper segmentation, the graph-based segmentation
method is implemented (Felzenszwalb and
Huttenlocher, 2004), which improves the detection
process of regions representing spots, whose pixels
maintain a distribution of varying intensity on SLAR
images with progressive intensity gradients. The
progressive intensity gradients in the image
represent the loss of the sensor sensitivity,
dependent on the resolution range (3),
c
t
2si
(3)
where γ is the angle of incidence of radar on the
scanned portion,
is the speed of light and
the
pulse duration. This data are restricted to the
authors, because they do not have access to SLAR
calibration. The resolution range is determined by
the value of the incidence angle of sensor, so each
pixel at near borders represents a larger portion of
scanning field than the pixels of the centre of the
image.
In addition, these gradients are exacerbated by
the problem of dissolution of the spill, due to
weather conditions and time. Some of the tests
consisted in applying to this algorithm some
modifications in order to address the problem, by
modifying the internal management of vectors which
stores the characteristics of intensity values of the
pixels of the scanned region.
The operation of this method is based on four
major steps. First, each pixel (i, j) of the image is
read and it is stored in a vector of differences of
intensity values for its four neighbour pixels (i + 1,
j), (i, j + 1), (i +1, j + 1) and (i + 1, j-1). Then, this
vector is ordered by the difference value from lowest
to highest. In the second step, the vector is read and
the pixels are added to a disjoint-set depending on
whether the difference among pixels is less than the
threshold defined at the outset. Thus, a disjoint-set
characterization in which its pixels are grouped
according to the difference among its four
neighbours is obtained. This feature provides the
algorithm with the ability to identify intensity
degradation areas as only one region. The third step
of the method involves removing groups of pixels
with a smaller size than those used at the outset in
the algorithm. Finally, a labelling of the generated
tree is done to obtain the binary matrix with the
performed segmentation. An example of the result of
applying this algorithm can be seen in Figure 3a.
3.4 J-image Segmentation
A J-image (Deng and Manjunath, 2001) is one in
which each element in the picture is defined, firstly,
by its intensity obtained as the mean difference of
variance of all tonalities of intensity within a
neighbourhood environment or window, and,
secondly, by the relative position of these tones in
relation to the central pixel of the window. To
perform this segmentation a normalization process
of the image must be made in which gray levels are
reduced, as done with the aforementioned co-
occurrence matrix.
ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods
444
Figure 2: a) Original snapshot. b) Pre-processed image. c) Energy image. d) Correlation image.
J-image is obtained as follows:


/
(4)
For this, first it is performed an image
transformation at N gray scales, each gray scale is
taken as a class.
With these values the total variance is calculated
as:


̅

(5)
where Z are all pixels of the normalized image, so
that z = (i, j) in which
∈, ̅ is the average
coordinate of the elements of Z. Next, the mean of
variance of each class is calculated as:



̅
∈


(6)
where
̅
is the average coordinate
class and C is
the number of gray levels used in the normalization.
Once the J-image is obtained, the segmentation is
performed using as seed pixels the J value less than
the selected threshold, obtaining the final result
shown in Figure 3b.
Figure 3: a) Graph-based segmentation for the Figure 2b
image. b) J-image segmentation for the Figure 2b image.
4 RESULTS AND ANALYSIS
To analyse the success rate of developed
segmentation methods, a ground truth from the
original images is created in which the oil spill
region is manually extracted, obtaining an image
with the oil spill area represented by white pixels
and the rest of the image is represented by black
pixels.
The objective of this work is to maximize the
True Positive rate, TP, which corresponds to the
well-segmented pixels which are part of the oil spill.
Additionally, another objective is to reduce the False
Positive rate, FP, which represents the erroneously
detected pixels. The last region of pixels taken into
account in this work is the False Negative rate, FN,
which represents the non-detected pixels part of the
oil spill. An example of these areas can be seen in
Figure. 4, in which the oil spill is represented by the
union of both TP and FN, the region segmented is
represented by the union of the zones TP and FP.
Figure 4: Example of representation of the zones labelled
for the analysis of detection.
Table 1 shows the results for the true positive
rate and false positive rate. It must be considered
that both the J-image segmentation for the pre-
processed image and the graph-based segmentation
for the original image present a high percentage of
false detection. Meanwhile, in the results with
False positive
True positive
False ne
g
ative
Oil Slick
(
a
)
Noise area
b
(
c
)
d
(a)
(b)
Oil Spill Detection using Segmentation based Approaches
445
Table 1: Accuracy rate of true and false positives from the snapshot of a video sequence (
) and for each scanning
sequence
.
Snapshot from video sequence
…
From each scanning sequence
TP (True Positive) FP (False Positive) TP (True Positive) FP (False Positive)
Image Type J-Image Graph J-Image Graph J-Image Graph J-Image Graph
Original 20.14% 73.97% 1.61% 27.46% 35.81% 85.30% 1.90% 26.96%
Pre-processed 92.03% 93.30% 14.42% 1.91% 81.16% 92.98% 15.19% 2.20%
Energy 90.34% 94.21% 2.48% 3.38% 80.16% 91.19% 2.78% 3.73%
Correlation 88.85% 95.40% 2.46% 3.11% 76.59% 93.21% 2.78% 3.44%
Figure 5: Example of ROC spaces for the scanning sequences. a) ROC space for the grap-based segmentation method with
the GLCM energy, b) ROC space for the J-image segmentation with the GLCM energy.
GLCM values, energy and correlation are more
consistent, showing a difference of 1% between the
J-image and the graph-based segmentation. Another
issue to consider is the high standard deviation
between the J-image and the graph-based
segmentation. Graph-based segmentation has
approximately twice the standard deviation value of
J-image.
Table 1 additionally shows the result for the true
positive rate and false positive rate, when the
algorithms are used with successive sequences of
scanning, each time with the results of the SLAR
sensor. Thereby, it can be seen that the FP rate is
similar to that previously obtained, but it is higher
using scanning sequences, than the static snapshots
generated from the video sequences. In the results
for the true positive rate, a high difference can be
seen between the results obtained for the TP shown
in Table 1.
Finally, for scanning sequences, Figure 5 shows
the results for ROC space of graph-based
segmentation and J-image segmentation. In both
cases, some detection problems are still present.
These problems correspond to the sequences where
there is nothing to segment but some segmentation
data is obtained. Although these bad results seem to
be high, they are made up by less than 10% of the
total results.
5 CONCLUSIONS
According to the results obtained in Section IV, the
graph-based segmentation is valid for the detection
of regions that may contain oil spills, but it generates
high percentages of false positive detection in non-
connected regions. Furthermore, the J-image
segmentation method has a slightly lower successful
rate for the TP rate than the other approach, but it
obtains much lower false positive percentages in the
segmentation process. Therefore, it enables us to
keep more information on the oil slick. We can
obtain features that describe the oil slick, such as
compaction, perimeter, elongation and so forth. The
best and most consistent results are obtained when it
is applied the correlation value with the J-image
segmentation using the static images generated from
(a)
(b)
ICPRAM 2017 - 6th International Conference on Pattern Recognition Applications and Methods
446
the acquisition and grouping of the scanning
sequences of the SLAR.
Future objectives are focused on studying
methods for the classification of the segmented
regions which represent potential oil spill areas.
ACKNOWLEDGEMENTS
This work was funded by Ministry of Economy and
Competitiveness and supported by Spanish project
(RTC-2014-1863-8) Thanks to INAER Helicopters
S.A.U. for provide the SLAR aerial data.
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