Oil Spill Detection using Segmentation based Approaches

D. Mira

, P. Gil

, B. Alacid

and F. Torres

Computer Science Research Institute, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690,

San Vicente del Raspeig, Alicante, Spain

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

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),











2sinγ

(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

ative

Oil Slick

(

)

Noise area

(

)

(

)

(

)

(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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