A novel threshold based approach of detecting oil spills on sea in synthetic aperture radar images
TNU Journal of Science and Technology
226(06): 10 - 17
A NOVEL THRESHOLD BASED APPROACH OF DETECTING OIL SPILLS
ON SEA IN SYNTHETIC APERTURE RADAR IMAGES
Nguyen Hung An*, Nguyen Tien Phat, Luong Thi Ngoc Tu
Le Quy Don Technical University
ARTICLE INFO
Received: 16/12/2020
Revised: 16/01/2021
Published: 04/02/2021
ABSTRACT
Nowadays, phenomena of oil spills commonly take place on rivers
and sea, and cause severe consequences on the water environment.
Therefore, detecting oil spills and providing early warnings of them
have received great interests for recent decades. There have been
many algorithms developed to identify oil spills using the Synthetic
Aperture Radar images because of their quality independence of
weather conditions and capability of event capture of a wide geometry
range. Among them, the threshold based methods are quite popular in
reality because of their implementation simplicity. However, these
algorithms provide relatively low accuracy. The paper proposed a
novel threshold based algorithm of oil spill detection in the Synthetic
Aperture Radar images. This threshold is a global one determined
according to statistical analysis of pixel intensities of the images and
their sizes. The simulation results of the proposed method on Python
software were compared with other methods, and proved that the
proposed method significantly improved accuracy.
KEYWORDS
Oil spill detection
SAR images
Adaptive Thresholding
Segmentation
Classification
MỘT CÁCH TIẾP CẬN MỚI DỰA TRÊN NGƯỠNG ĐỂ PHÁT HIỆN VỆT DẦU
LOANG TRÊN BIỂN TRONG CÁC ẢNH RA ĐA MẶT MỞ TỔNG HỢP
Nguyễn Hùng An*, Nguyễn Tiến Phát, Lương Thị Ngọc Tú
Trường Đại học Kỹ thuật Lê Quý Đôn
THÔNG TIN BÀI BÁO
Ngày nhận bài: 16/12/2020
Ngày hoàn thiện: 16/01/2021
Ngày đăng: 04/02/2021
TÓM TẮT
Ngày nay, hiện tượng dầu loang trên biển diễn ra khá phổ biến trên
các sông ngòi, biển và gây ra các hậu quả nghiêm trọng cho môi
trường nước. Vì vậy, việc phát hiện các vết dầu loang và đưa ra các
cảnh báo sớm về hiện tượng này nhận được sự quan tâm rất lớn trong
vài thập kỷ gần đây. Thực tế, người ta đã phát triển nhiều thuật toán
nhận dạng vết dầu loang trên biển sử dụng ảnh ra đa mặt mở tổng
hợp bởi vì chất lượng ảnh ít phụ thuộc vào điều kiện thời tiết, ngày
và đêm và có khả năng bắt giữ các sự kiện trên quy mô địa lý rộng
lớn. Trong đó, các thuật toán dựa trên ngưỡng khá phổ biến trong
thực tế vì thực hiện đơn giản. Tuy nhiên, những thuật toán này nói
chung có độ chính xác không cao. Bài báo này đề xuất một thuật toán
mới dựa trên ngưỡng để phát hiện vết dầu loang trên biển. Đây là
ngưỡng toàn cục được xác định dựa trên phân tích thống kê về cường
độ các điểm ảnh và kích thước của ảnh. Các kết quả mô phỏng của
thuật toán trên phần mềm Python được so sánh với các phương pháp
khác và chứng minh rằng phương pháp đề xuất đã cải thiện đáng kể
độ chính xác.
TỪ KHÓA
Phát hiện vết dầu loang
Ảnh SAR
Ngưỡng thích nghi
Phân đoạn
Phân loại
10
TNU Journal of Science and Technology
226(06): 10 - 17
1. Introduction
Oil spill detection on sea has been an attractive topic receiving great interests for recent
decades because of practical demands and its challenges brought to image processing experts in
particular and scientists in general. Annually, a huge amount of oil is poured into oceans due to
oil leakages from ship collisions and accidents at manufactures producing petroleum on sea.
More seriously, oil spills easily spread on wide areas because of strong winds and flows in ocean.
Therefore, they could cause severe damages of the underwater environment if there is no solution
to early warning and detecting them.
There is a variety of methods developed to identify, locate, and track oil spills on sea.
However, there are two popular groups of methods of oil spill detection, namely optical image
based [1] - [3], and Synthetic Aperture Radar (SAR) image based methods [4] - [6]. The latter
methods are more advantageous than the former ones in terms of their coverage and relative
independence of weather conditions. In fact, the detection of oil slicks using SAR image
processing techniques is very popular in practice and specially applied in remote sensing systems
of monitoring oil spills in wide areas.
Due to oil slicks occurring in SAR images as dark regions, a great challenge of oil spill
detection is to separate dark areas corresponding oil slicks with other dark ones without deriving
from real oil spills. In addition, reduction of impacts of speckle noises on the quality of SAR
images is another challenge [7] - [9]. In order to detect oil spills, threshold based methods were
early developed by Otsu in 1979 [10] - [12]. Although their accuracy is limited, they are still
widely utilized in practice for its computational simplicity. The classical method for oil spill
identification is Otsu’s method [10], which is based a global single threshold value computed
from the brightness distribution of an entire image to identify black areas corresponding to
potential candidates of oil spills. However, the Otsu’s algorithm only works well with gray level
images with only bi-modal distribution in the gray level histogram, and fails when the histogram
is not bi-modal but is uni-modal or multi-modal [11].
Another threshold based method is proposed by Bradley and Roth in 2007 [13] in which the
threshold values were firstly locally computed at a neighbourhood of the pixel under consideration,
then separating a pixel if it belongs to dark regions or not. Generally, adaptive thresholding
solutions based on local thresholds; for example, using mean and Gaussian filters are also quite
popular in image segmentation tasks because of their implementation simplicity [14].
This paper developed a novel algorithm of oil spill detection in SAR images, which is based
on determining a global threshold to classify which pixels belong to dark regions or not. This
algorithm can works well for both bi-modal and non-bimodal gray level images.
The remainder of the paper is organized as follows. Section 2 presents the methodology for the
proposed method. In Section 3, we present some simulation results to evaluate the proposed
algorithm performance in comparison with three other algorithms using Python software. In Section
4, we draw conclusions from the results of our evaluation and outline areas for future work.
2. Research methodology
Detection of dark spot is a first stage of the procedure of identifying oil spills in SAR images.
The paper concentrates on detecting darks area in the SAR images. The block diagram of the
system detecting dark spots in SAR images shown in Figure 1. In this paper, we focus on
developing a new algorithm for the block of Adatptive Thresholding.
Laplace
Filter
Adaptive
Thresholding
SAR Image
Result
Figure 1. The block diagram of system detecting dark spots in SAR images
11
TNU Journal of Science and Technology
226(06): 10 - 17
There are many adaptive thresholding methods for image segementation such as adaptive
mean thresholding (AMT), adaptive Gaussian thresholding (AGT), adaptive median thresholding,
and adaptive thresholding using integral image. The first three thresholding methods standardized
in the library of software Python 3.0 were used to compare with the proposed method.
In this paper, we proposed a new global thresholding method based on statistic probabilty
theory. This global threshold is determined for every SAR image to identify oil spills. The
proposed threshold is computed as a function of image sizes and pixel intesities. The global
threshold assumed as is calculated as follows.
1 +2
(1)
=
2
2
I
ij
i=1:x
j=1: y
I −
ij
xy
i=1:x
I
j=1: y
ij
i=1: x
j=1: y
1 =
−
(2)
xy
xy
2
I
ij
i=1: x
j=1: y
I −
ij
xy
i=1: x
j=1: y
2 = n −
(3)
xy
where x is the number of pixels of an image row, and y is the number of pixels of an image
column. Iij is the pixel intensity at the position (i, j). n is the pixel intensity which is the most
repeated one in the image.
Based on the threshold computed by the equations (1), (2) and (3), pixels whose intensities
lower than this threshold were assumed as dark points, else pixels as the white background.
According to a oil feature in SAR images, the rigthmost dark areas are potential candidates of
high probability to be oil spills.
3. Simulation results and discussion
The simulations were perrformed on Python software with two stages. In the first stage, the
simulations was implemented with the first original image shown in Figure 2(a) for four different
methods which are adaptive mean thresholding (AMT), adaptive Gaussian thresholding (AGT),
Otsu method, and the proposed method. In the second stage, the simulations was performed with
the second original image shown in Figure 2(d) for these four methods. In addition, the first three
methods were performed according to the standard library functions in Python software.
The SAR images selected to simulate the proposed algorithm of oil spill detection are shown
on Figure 2(a), and 2(d). The original images are downloaded from the official website of
European Satellite Agency (ESA) [15], which is European space and space-technology research
organization. Their respective ground truth images shown in Figure 2(b) and 2(e) were generated
based on the combination of prior knowledge and photo interpretation. These ground truth
images were used to compare with simulation result images of investigated methods. These
comparisons are to evalulate the accuracy of proposed method.
12
TNU Journal of Science and Technology
226(06): 10 - 17
(a)
(d)
(b)
(e)
(c)
(f)
Figure 2. Two original images of oil spills (a) and (d), and their respective ground truth images (b) and (e).
The histograms (c) and (f) are respectively ones of the original images (a) and (d). The bold rectangles in
Figures (a), (b), (d) and (e) only describes the boundary of the image and are not real data.
The image in Figure 2 (a) is bi-modal one with its histogram shown in Figure 2(c), while the
image in Figure 2(d) is a multi-modal one which has the histogram shown in Figure 2(f). Using
these two images is to prove the proposed algorithm working well with both bi-modal and multi-
modal images. The simulation results for the first stage are shown in the Figure 3. The simulation
13
TNU Journal of Science and Technology
226(06): 10 - 17
results of four methods are images with two intensities of 0 corresponding black pixels and 255
corresponding to white pixels, in which black pixels were assumed as oil pixels. Evaluating the
accuracies of the methods are performed by comparing the result images generated for the four
methods with the corresponding ground truth image including adaptive mean thresholding,
adaptive Gaussian thresholding methods, and Otsu method.
For instance, based on comparison of the ground truth image shown in Figure 2(b) with Figure
3(a), (b), (c) and (d), it can be qualitatively seen that, Figure 3(d) shows a better result than the
remaining figures. In other words, for the qualitative view, the proposed method provided the best
approximation to the ground truth image in Figure 2(b) among compared methods.
(a)
(b)
(c)
(d)
Figure 3. The simulation results of detecting oil spills in the image of Figure 2(a) by (a) the adaptive mean
threshoding method, (b) the adaptive Gaussian thresholding method, (c) Otsu’s method, and (d) the proposed
method. The bold rectangles in images only describe the boundary of images and are not real data
For a quantitative evaluation, the proposed method was compared with the three above
methods in terms of root mean square error (RMSE). More particularly, the RMSEs between
each method’s resultant images and the ground truth image shown in Figure 2(b) were compared
together The achieved results were listed in Table 1. It is obviously seen on Table 1 that the
proposed method provides the smallest RMSE compared to the remaining methods.
14
TNU Journal of Science and Technology
226(06): 10 - 17
(a)
(b)
(c)
(d)
Figure 4. The simulation results of detecting oil spills in the image of Figure 2(b) by (a) adaptive mean
thresholding method, (b) adaptive Gaussian thresholding method, (c) Otsu’s method (a), and (d) the
proposed method. The bold rectangles in images are not real data.
Table 1. RMSEs for four methods using the bi-modal image shown in Figure 2(a)
Method
AMT method
AGT method
Otsu method [10]
Proposed method
RMSE
1949.31
2126.25
1168.89
548
Similarly, the simulations were performed on the second original image shown in Figure 2(d)
in the second stage, which is the multi-modal image. The achieved results for the four methods
were shown in Figure 4.
Qualitatively, the Figure 4(d) is the best approximation to the Figure 2(d). In other words,
the proposed method provides the qualitatively best result among four compared methods. In
order to evaluate the accuracy of the proposed method, the proposed method was compared
with the three above methods in terms of RMSE. The comparison results were shown in Table
2. It can be seen in the Table 2 that the RMSE of the proposed method is the smallest among
four investigated methods. It means that the proposed method achieves the higher accuracy
than the remaining methods.
15
TNU Journal of Science and Technology
226(06): 10 - 17
Table 2. RMSEs for four methods using the multi-modal image shown in Figure 2(d)
Method
RMSE
1774.40
1872.57
2228.97
342.66
AMT method
AGT method
Otsu method [10]
Proposed method
In the similar way, the four above investigated methods were also evaluated by using eight other
SAR images with all three different histogram shapes which are uni-modal, bi-modal and multi-
modal. Each image was alternately applied to these four methods to estimate their RMSE values.
As a result, there were total ten RMSE values corresponding to the ten SAR images applied to
each method. Averaging these ten RMSE values provides an average RMSE value for each
method. The average RMSE value was used to compare accuracy of the investigated methods.
The simulations showed that the RMSE values obtained by the proposed method are smaller
than all those obtained by the remaining methods. More particularly, the average RMSE value of
the proposed method calculated over the ten simulated SAR images is less than all those of the
three remaining methods computed over the same ten images, as shown in Table 3.
Table 3. Average RMSE values of four methods calculated over the ten different SAR images of oil spills
Method
Average RMSE
2014.79
AMT method
AGT method
Otsu method [10]
Proposed method
2057.11
1480.98
559.92
In summary, the proposed method works well for the uni-modal, bi-modal and multi-modal
images, and provides higher accuracy than other methods including the adaptive mean,
thresholding, adaptive Gaussian thresholding, and Otsu methods.
4. Conclusion
The paper developed a novel algorithm for detecting oil spills on sea, which is based
statistically selecting a global threshold to separate if a pixel is a dark spot in SAR images.
The results of the proposed method were compared in the qualitative and quantitative ways
with the adaptive mean thresholding, and adaptive Gaussian thresholding methods and Otsu’s
method for both bi-modal and non-bimodal gray level images. The results of the proposed
method provide higher accuracy in comparison with the three remaining methods.
In the near future, we focus on deploying this proposed algorithm in digital signal processing
hardware so that it can be applied in practice for real-time applications.
REFERENCES
[1] S. Liu, M. Chi, Y. Zou, A. Samat, J. A. Benediktsson, and A. Plaza, "Oil spill detection via
multitemporal optical remote sensing images: A change detection perspective," IEEE Geoscience and
Remote Sensing Letters, vol. 14, pp. 324-328, 2017.
[2] M. S. Lee, K. A. Park, H. R. Lee, J. J. Park, C. K. Kang, and M. Lee, "Detection and dispersion of oil
spills from satellite optical images in a coastal bay," IEEE International Geoscience and Remote
Sensing Symposium (IGARSS), Beijing, China, 2016.
[3] C. S. L. Grimaldi, I. Coviello, T. Lacava, N. Pergola, and V. Tramutoli, "Near real time oil spill
detection and monitoring using satellite optical data," IEEE International Geoscience and Remote
Sensing Symposium, Cape Town, South Africa, 2009.
[4] K. W. Bjerde, A. H. S. Solberg, and R. Solberg, "Oil spill detection in SAR imagery," IGARSS'93-
IEEE International Geoscience and Remote Sensing Symposium, Tokyo, Japan, 1993.
16
TNU Journal of Science and Technology
226(06): 10 - 17
[5] K. N. Topouzelis, "Oil spill detection by SAR images: dark formation detection, feature extraction and
classification algorithms," Sensors, vol. 8, pp. 6642-6659, 2008.
[6] A. B. Salberg, O. Rudjord, and A. H. S. Solberg, "Oil spill detection in hybrid-polarimetric SAR
images," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, pp. 6521-6533, 2014.
[7] J. R. Sveinsson, and J. A. Benediktsson, "Speckle reduction and enhancement of SAR images using
multiwavelets and adaptive thresholding," Image and Signal Processing for Remote Sensing V,
International Society for Optics and Photonics, 1999, pp. 239-250.
[8] B. B. Saevarsson, J. R. Sveinsson, and J. A. Benediktsson, "Speckle reduction of SAR images using
adaptive curvelet domain," IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing
Symposium. Proceedings (IEEE Cat. No. 03CH37477), IEEE, 2003, pp. 4083-4085.
[9] V. Santhi, C. Mouli, and A. Thangavelu, "Speckle Reduction of SAR Images using Adaptive Sigmoid
Thresholding and Analysis of various Filtering Techniques," International Journal of Computer
Applications, vol. 46, pp. 9-16, 2012.
[10] N. Otsu, "A threshold selection method from gray-level histograms," IEEE transactions on systems,
man, and cybernetics,vol. 9, pp. 62-66, 1979.
[11] H. F. Ng, "Automatic thresholding for defect detection," Pattern recognition letters, vol. 27, pp. 1644-
1649, 2006.
[12] X. Xu, S. Xu, L. Jin, and E. Song, "Characteristic analysis of Otsu threshold and its applications,"
Pattern recognition letters, vol. 32, pp. 956-961, 2011.
[13] D. Bradley, and G. Roth, "Adaptive thresholding using the integral image," Journal of graphics tools,
vol. 12, pp. 13-21, 2007.
[14] B. Whalen, "Adaptive Thresholding Using Quadratic Cost Functions," International Journal of Image
Processing (IJIP), vol. 13, p. 76, 2019.
[15] ESA, Prestige oil spill. [Online]. Available: https://earth.esa.int/web/guest/data-access /sample-data/-
17
8 trang
17880
Free
Bạn đang xem tài liệu "A novel threshold based approach of detecting oil spills on sea in synthetic aperture radar images", để tải tài liệu gốc về máy hãy click vào nút Download ở trên
File đính kèm:
- a_novel_threshold_based_approach_of_detecting_oil_spills_on.pdf
