Journal of Oceanology and Limnology   2021, Vol. 39 issue(4): 1198-1210     PDF
Institute of Oceanology, Chinese Academy of Sciences

Article Information

WANG Wandi, SHENG Hui, CHEN Yanlong, LIU Shanwei, MAO Jijun, ZENG Zhe, WAN Jianhua
A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images
Journal of Oceanology and Limnology, 39(4): 1198-1210

Article History

Received Mar. 5, 2020
accepted in principle Jun. 15, 2020
accepted for publication Aug. 13, 2020
A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images
Wandi WANG1, Hui SHENG1, Yanlong CHEN1,2, Shanwei LIU1, Jijun MAO3, Zhe ZENG1, Jianhua WAN1     
1 China University of Petroleum, Qingdao 266580, China;
2 National Marine Environmental Monitoring Centre, Dalian 116023, China;
3 Surveying and Mapping Institute of Shandong Province, Jinan 250102, China
Abstract: Marine oil spills are among the most significant sources of marine pollution. Synthetic aperture radar (SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identification. However, speckle noise, weak boundaries, and intensity inhomogeneity often exist in the oil spill regions of SAR imagery, which will seriously affect the accurate identification of oil spills. To enhance marine oil spill segmentation of SAR images, a fast, edge-preserving framework based on the distance-regularized level set evolution (DRLSE) model was proposed. Specifically, a bilateral filter penalty term is designed and incorporated into the DRLSE energy function (BF-DRLSE) to preserve the edges of oil spills, and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods, the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy (97.83%). In addition, using an appropriate initial box boundary for the DRLSE method accelerated the global search process, improved the accuracy of oil spill segmentation, and reduced computational time. Therefore, the results suggest that the proposed framework is effective and applicable for marine oil spill segmentation.
Keywords: level sets    bilateral filter    marine oil spill segmentation    synthetic aperture radar (SAR) imagery    

Marine oil spill accidents, which have caused various types of environmental damage, have frequently occurred at different scales (Rischard, 2001; Chiau, 2005; Wang et al., 2019). Marine oil pollution is generally due to illegal ship discharges and large ship and oil rig accidents, such as the British Petroleum (BP) Deepwater Horizon oil spill accident (in 2010, northern Gulf of Mexico). Alves et al. (2016) stated that in the Eastern Mediterranean, during oil spill accidents, offshore oil spills may spread and reach the shoreline very quickly under certain weather and oceanographic conditions. Monitoring and research on offshore oil spills can solve the marine ecological and environmental damage caused by oil spills (Cisneros-Montemayor and Sumaila, 2010; Cheng et al., 2011). Synthetic aperture radar (SAR) is a well-established remote sensing tool for the detection of oil spills, and SAR has been proven to useful in clean-up operations after oil spill events (Jackson and Apel, 2004; Solberg, 2012).

The segmentation of marine oil spill regions in SAR images is an important research topic (Topouzelis and Psyllos, 2012; Gautama et al., 2016; Espeseth et al., 2020). However, case studies using SAR data for marine oil spill accidents are insufficient. Unlike the state-of-the-art deep learning strategies that require labeled large amounts of oil-spill sample data to train complex models, the level set algorithm can directly segment a single SAR image. In recent years, the level set algorithm has been widely used in SAR images segmentation (de Andrade et al., 2017). For marine oil spill segmentation from SAR images, Karantzalos and Argialas (2008) presented a geometric level-set segmentation method for the detection of possible oil spills in SAR imagery. Ganta et al. (2012) proposed a level set model based on illumination reflectance to segment the oil spill areas in SAR satellite images. These two methods could not avoid the impact of speckle noise in SAR images. Shao et al. (2013) presented an integrated method involving spatial-based spatial fuzzy C-means and a level-set method for oil spill segmentation. Liu et al. (2015) presented a variational level set SAR image obtained with an oil spill detection method based on fuzzy clustering. These two methods use the boundary of the clustering result as the initial border of the level set. However, there are situations in which the initial border intersects with the real water-oil boundary due to the inaccuracy of clustering, which leads to unstable curve iterations and inaccurate segmentation results. To address random noise issues, Yang et al. (2014) developed a shape-initialized and intensity-adaptive level set method. To reduce the effect of noise, they proposed an efficient Markov random field (MRF) embedded level set method to improve the robustness of segmentation (Yang et al., 2015). These two methods lose the edge details of areas with low backscattering. Wu et al. (2017) presented a two-stage method for the segmentation of oil spill images. In the first stage of this method, a filtering technique is applied to suppress the backscattering of the SAR image. However, filtering blurs the edges of the oil spill area. Song et al. (2018) presented an improved active contour model for marine oil spill segmentation. In this method, SAR images are filtered twice, resulting in the loss of the edge details of oil spills. Li et al. (2014) proposed an improved reconstruction algorithm for Compact-Polarimetry (CP) SAR on oil spill detection. Zhang et al. (2018) proposed a semi-empirical ocean surface model for Canadian RADARSAT Constellation Mission (RCM) CP mode, and suggested that the new CP mode is helpful for monitoring of the ocean oil spill. However, due to the diversity of SAR data acquisition and ocean surface conditions, it is difficult to propose a general reconstruction model to achieve accurate extraction of oil spill.

To solve the aforementioned issues and improve oil-spill segmentation performance, we proposed a fast, edge-preserving, distance-regularized level set evolution model with bilateral filtering (BF-DRLSE) for oil-spill segmentation from SAR images. The proposed BF-DRLSE algorithm mainly involves a bilateral filtering scheme and a better initial box boundary. The BF-DRLSE algorithm can smooth the SAR image without blurring the oil spill edge, and provides a better initial frame boundary for the DRLSE model to accelerate the model convergence.

2 MATERIAL AND METHOD 2.1 Remote sensing data

In this study, two sets of remotely sensed Polarimetric Radarsat-2 data were used. The Radarsat-2 SAR images over the ocean were acquired via fine quad-polarization imaging of the Gulf of Mexico. The first scene was acquired on May 8, 2010 with a space resolution along the azimuth and a range direction resolution of approximately 5 m. The second scene was acquired on May 8, 2015 with the same spatial resolution during an oil spill event. Tables 1 & 2 present information for the two scenes. To validate the effectiveness of the proposed BF-DRLSE algorithm, SAR images with Vertical Vertical (VV)-polarization that contain different marine oil spill regions were used as the test dataset (Figs. 1 & 2).

Table 1 Main information for the Radarsat-2 data used in this study (Dataset 1)
Table 2 Main information for the Radarsat-2 data used in this study (Dataset 2)
Fig.1 Marine oil spill images from RadarSat-2 SAR in VV-polarization backscatter imaging mode used in this study (Dataset 1) The images of Dataset 1 were determined to contain oil spills and water. The type of oil is crude oil.
Fig.2 Marine oil spill images from RadarSat-2 SAR in VV-polarization backscatter imaging mode used in this study (Dataset 2) The images of Dataset 2 were determined to contain oil spills and water. The type of oil is crude oil.

Because it is a tremendous amount of work to classify the total test area based on geographical knowledge and visual interpretation, we restricted the accuracy assessment to three areas of interest (AOIs) for two sets of remotely sensed data.

2.2 Methodology 2.2.1 Improved DRLSE with a bilateral filter penalty term (BF-DRLSE algorithm)

Osher and Sethian (1988) proposed the level set method to track moving boundaries in 1988, in which the contour is formed by inserting a 2D plane into a 3D surface to represent the level set of the contour. Figure 3 shows that the 3D surface intersects the 2D plane exactly where the curve is located. The red 3D surface is called the level set function (LSF). The black boundary of the 2D white plane is the collection of all points at height zero, so it is called the zero level set. The level set algorithm attempts to move the contour of a zero LSF to the object boundaries by minimizing a cost function.

Fig.3 Schematic diagram of level set method

A contour can be expressed as (x, y, t), and the level set evolution equation can be expressed as:

where ϕ represents the zero level set of the level set function, F is the speed function that controls the motion of the contour, t is a temporal variable, and is the gradient operator.

In conventional level set methods, the LSF typically develops irregularities during its evolution, and these irregularities generally cause numerical errors that influence the signed distance property, eventually affecting the stability of level set evolution. To solve this problem, reinitialization solution was introduced to restore the regularity of the LSF and maintain the stability of the level set evolution (Sussman et al., 1994; Sethian, 1999; Osher and Sethian, 1988; Li et al., 2010). A standard method for reinitialization is to solve the steady state form of the following evolution equation:

where sign(ϕ0) is the sign function, ϕ0 is the LSF to be reinitialized. The steady state solution of evolution equation is a signed distance function under ideal conditions (Sussman and Fatemi, 1999; Li et al., 2010).

However, in the process of reinitialization using function of ∂ϕ/∂t, the level set function is periodically corrected, which is a very complex computation and has the high temporal complexity of the algorithm (Zhang et al., 2013). To overcome this issue, Li et al. (2010) proposed a DRLSE model with a distance regularization term and an external energy term. DRLSE method can drive the level set function close to a signed distance function without reinitialization, which reduce the number of iterations and computational time and ensures sufficient numerical accuracy (Li et al., 2010).

The DRLSE model energy formula with distance regularization is defined by:

where Rp(ϕ) is the level set regularization term, Eext(ϕ) is the external energy, μ is a constant.

Rp(ϕ) is defined by:

where p is a potential (or energy density) function. The definition of the potential p for distance regularization is as follows.

The energy Eext(ϕ) is designed to be at a minimum when the zero level set of the LSF is located at a desired position. Rp(ϕ) eliminates the need for reinitialization by correcting the deviation between the level set function ϕ and the sign distance function sign(ϕ0).

Let I denote an original marine oil spill SAR image in the domain Ω. The convolution in Rp(ϕ) is used to smooth the SAR image and reduce noise. This edge indicator g roughly captures the marine oil spill boundaries from I. g is defined by:

where Gσ is a Gaussian kernel.

Edge indicator g is included in the energy function for an LSF proposed by Li et al. (2010), and the energy functional Eext(ϕ) was defined as follows:

where λ and α are the coefficients of the energy functionals Lg(ϕ) and Ag(ϕ), respectively, δ(ϕ) and H(-ϕ) are the Dirac delta function and the Heaviside function, respectively. The term Lg(ϕ) is the line integral. In this study, Lg(ϕ) is minimized when the LSF contour is located at the marine oil spill boundaries in I. The energy functional Ag(ϕ) is the weighted area of the non-oil region of I in this study and is used to slow the shrinking or expanding of the zero level contour when it reaches the marine oil spill boundaries.

As an outstanding remote sensing tool, SAR can provide high-resolution images of the marine surface for marine oil spill research. However, speckle noise can seriously affect SAR image quality (Argenti et al., 2013). The DRLSE model is an edge-based level set evolution method, and the edge indicator function g is an important part of its energy function. To reduce the speckle noise influence, image smoothing with Gaussian filter operator Gσ in the edge indicator function g is performed before level set evolution in the DRLSE method. However, this smoothing tends to blur the boundaries of marine oil spill regions and reduce the segmentation accuracy of marine oil spill regions.

To solve overcome the negative impacts of speckle noise in the DRLSE method, a bilateral filtering penalty term is proposed in this study. Because the well-known bilateral filtering operator has proven to be a remarkable denoising and edge-preserving method (Tomasi and Manduchi, 1998; Zhang et al., 2016; Fawwaz et al., 2018; Routray et al., 2018), it is adopted in this study to achieve noise suppression, precisely preserve oil spill edges, and improve the performance of the DRLSE model.

To simultaneously smooth the image and preserve edge details, a bilateral filter was proposed by Tomasi and Manduchi (1998). The bilateral filter is defined as follows:

where i and j are the pixel indexes, (k, l) is the center point of the template, h(i, j) is the denoised intensity of pixel (i, j), f(k, l) is the intensity of the pixel at (k, l), and w(i, j, k, l) is the weight coefficient. The weights w(i, j, k, l) are computed based on the neighborhood approach. For the center sample f(k, l), the weight w(i, j, k, l) is computed by multiplying the following two factors:

where σd12 and σd22 re smoothing parameters, the scale parameter in the Gaussian kernel is set to 1, and the Gaussian kernel is set to 15. The final weight w(i, j, k, l) is obtained by multiplying these two factors as follows:

In the filtering process of the bilateral filter, the SAR image is convoluted based on the template with weight w(i, j, k, l):

where is the filtered SAR image, and I is the original input SAR image to be filtered.

According to the above discussion, the improved DRLSE energy formulation with a bilateral filtering penalty term is defined by:

where β is a constant, and Eb(ϕ) is the bilateral filtering penalty energy.

In this study, Eb(ϕ) is designed as:

where is the SAR image smoothed with the bilateral filter.

The energy functional Eb(ϕ) computes the convolution of SAR images with a bilateral filter. For the special case of g=1, this energy is exactly associated with the edge of the oil spill. Eb(ϕ) will reduce the iteration speed when the level set contour approaches the oil spill boundaries. Eb(ϕ) is introduced to smooth SAR images without blurring oil spill edges. Here, g is used to slow the shrinking or expanding of the level set curve.

In this paper, the Dirac delta function δ and Heaviside function H in the functional Eb(ϕ) are approximated by the following smooth functions δε and Hε, which are defined as follows:

where the parameter ε is set to 1.5.

The steady state solution of the gradient flow equation is the minimized energy function of E(ϕ). The gradient flow equation is defined by:

where ∂E/∂ϕ is the Gâteaux derivative of the functional E(ϕ).

The Gâteaux derivative of the functional Rp as follows:

According to the above analysis, the gradient flow equation of E(ϕ) can be reformulated as follows.

Minimize the energy function Eb(ϕ) and find the steady-state solution of the gradient flow equation.

According to the gradient flow equation Eb(ϕ) and E(ϕ), the BF-DRLSE energy function with bilateral filtering penalty term can be reformulated as follows.

The minimization of the energy functional with a bilateral filtering penalty term can be obtained by the following iteration process.

The flow diagram of the level set evolution in the BF-DRLSE algorithm is illustrated in Fig. 4. The pseudo-code is given in Table 3.

Fig.4 The flowchart of the algorithm
Table 3 Description of the pseudo-code of the BF-DRLSE algorithm
2.2.2 Improved initial box boundaries

The DRLSE model can iterate the drive curve of any shape to the target boundary. The DRLSE model can drive the initial box boundary of any shape to the target boundary. However, if the initial box boundary is far from the target boundary, the curve evolution may require more complicated calculations that result in slower iteration of the level set curve and affect the accuracy of the final segmentation. To improve the estimation of the initial target contour positions, a method of obtaining a threshold using a histogram of VV-polarization SAR images is introduced. The advantages of the method include its simple computations and easy implementation. In this method of estimating the initial contours, the optimal threshold is selected using the 2-mode method between the peaks and valleys. The improved initial box boundaries accurately surround the oil spill boundary in this approach, making it faster and more accurate than the original BF-DRLSE algorithm. Notably, image-driven forces are used to drive the level function to the boundaries of the object of interest rather than assuming a rectangular initial box boundary. The concept of this method is demonstrated in Fig. 5, and the initial guess of the initial box boundary is shifted by image-driven forces to the boundaries of the oil spill.

Fig.5 The concept of the proposed method a. rectangular initial box boundary; b. improved initial box boundary; c. oil spill segmentation results.
3 RESULT AND DISCUSSION 3.1 Running efficiency of the BF-DRLSE algorithm with an improved initial box boundary

The experimental results for the AOIs are shown in Fig. 6. Significant differences were observed between the convergence results of the DRLSE and BF-DRLSE algorithms. The BF-DRLSE model ran for 200 iterations and converged, and the DRLSE model ran to the more than 4 500 iterations. Figure 6 show the convergence results with different level set initializations using the DRLSE method and the BF-DRLSE algorithm, respectively. The first column shows different level set initializations. The second column shows the convergence results of the DRLSE method and the BF-DRLSE algorithm. Figure 6 shows that most of the boundaries between seawater and oil spills can be detected using both initial box boundary. However, the final convergence result depends on the initialization of the level set. The DRLSE method ignores correlations among neighboring pixels; thus, the zero LSF often stops iterating at noisy points, which leads to inaccurate identification, as shown in red. The BF-DRLSE algorithm can use an improved initial box boundary to accurately surround the oil spill boundary. Notably, the BF-DRLSE method is much more robust against noise than the DRLSE method, and it can preserve more details. Moreover, the BF-DRLSE algorithm obtains more-accurate results and has a shorter execution time than the DRLSE method.

Fig.6 Results of the DRLSE and BF-DRLSE method at the initial and final times The red borders are the outlines of the level set algorithms DRLSE and BF-DRLSE. The red border is the initial border. The yellow dashed rectangle is the region of interest in Section 3.2.

Figure 7 illustrates the runtime and number of iterations for different strategies. The execution times of the algorithm based on the rectangular initial frame are longer than those based on the improved initial contour when identifying the oil spills from all AOIs. These experimental results confirm that the improved initial box border provides a relative guarantee that the BF-DRLSE algorithm will achieve ideal convergence, thereby reducing the convergence time for oil spills in the AOIs.

Fig.7 Time index and number of iterations for different initial box boundaries of AOI1 to AOI3
3.2 Noise suppression without blurring edges in SAR images

The BF-DRLSE algorithm can precisely preserve the oil spill edges in the segmentation results. The selected region in the dashed rectangle of the AOI1 image in Fig. 6 is used as an example, and the pixel value distribution in the gray frame in the figure is shown in Fig. 8. The boundary of an oil spill is often very weak and blurry due to the low contrast between the seawater and the oil spill (Jubai et al., 2006; Song et al., 2013), leading to the intensity distributions of the oil slick region and seawater having considerable overlap. The DRLSE method considers only the intensity distribution information, which limits its performance. Figure 8a indicates that the edges are greatly affected by noise, resulting in a very rough boundary between seawater and an oil spill. However, the proposed BF-DRLSE algorithm considers the correlations among neighboring pixels, and it does not stop the iteration of the zero-order set function at noisy points; thus, the boundary between seawater and an oil spill is accurately identified. Figure 8b clearly shows that the BF-DRLSE algorithm proposed in this study can be used to denoise SAR images and effectively preserve the variations in SAR image edge values so that the edges are not blurred.

Fig.8 Image pixel value distribution a. pixel value distribution with the DRLSE method; b. pixel value distribution with the BF-DRLSE algorithm.

To analyze the performance of the BF-DRLSE algorithm quantitatively and objectively, the image quality metrics, such as the equivalent number of looks (ENL) (Adler et al., 2004) and the edge save index (ESI) (Tang et al., 1996), were calculated. ESI reflects how well the edges are preserved in an image after noise reduction compared with those in the original image.

A high value of ESI indicates that edge details are effectively conserved. The corresponding formula is as follows:

where pi is the original image, p0 is the despeckled image, i is the row number of the image and j is the column number. The ESI values are given in Table 4.

Table 4 The assessment of edge preservation effectiveness in the SAR images based on ESI

ENL is a metric that quantifies noise reduction in homogeneous areas of SAR images, and the ENL values increases as noise reduction improves. The ENL equation is defined as follows:

where u is the mean of the pixel values in the AOI, and σ is the standard deviation of pixel values. The ENL vales are presented in Table 5.

Table 5 The assessment of filtering effectiveness for an SAR images based on ENL

Tables 4 & 5 present the results of the quantitative metrics used to assess images based on the DRLSE and BF-DRLSE algorithms. The tables clearly show that the edge preservation and denoising results for the BF-DRLSE algorithm are better than those for the DRLSE method.

3.3 Segmentation results and accuracy analysis

To comprehensively validate the effectiveness of the proposed BF-DRLSE algorithm in marine oil spill segmentation, we compared the BF-DRLSE algorithm with the DRLSE method, the DRLSE after bilateral filtering (BF) method, the Otsu method, and the Kittler and Illingworth (KI) method. Three state-of-the-art speckle filters and segmentation procedures were implemented for comparison: the polarization SAR simultaneous sparse coding filter, which was incorporated into the DRLSE energy function (PSSC-DRLSE) (Xu et al., 2016), the efficient MRF embedded level set method (MRF-LSM) (Yang et al., 2015), and the improved active contour model (ACM) (Song et al., 2018). Note that the goal of this study is not to rank these technologies based on performance but rather to judge whether the algorithm is suitable for the SAR image in terms of the accuracy of the experimental results. Figure 9 compares the segmentation results for three sets of experimental data using the BF-DRLSE algorithm, MRF-LSM, the DRLSE model, the DRLSE after BF method, the Otsu method, the KI method, the PSSC-DRLSE model, and the improved ACM method. Figure 9i demonstrates the interpretation images. Figure 9a shows that the DRLSE model generally reaches the boundaries of oil spill regions, but the segmentation results contain many small detected regions that are not oil spill regions, which illustrates that the DRLSE model is sensitive to noise; therefore, it often stops the iteration of the zero LSF, leading to many false alarms. The segmentation results obtained with the DRLSE after BF method shown in Fig. 9b exhibit poor segmentation performance. Figure 9c-d illustrates the results of the Otsu and KI methods, respectively. Although the results are visually better than those of DRLSE after BF, many false alarms still exist. Figure 9h displays that the MRF embedded level set model can roughly yield continuous boundaries between oil spills and seawater. Figure 9f presents that PSSC-DRLSE could basically find the contour of the oil spill, but there are many wrong contours. The robustness of DRLSE itself and the excessive de-speckle capability of the PSSC filter result in loss of the edge details of oil spills. Figure 9g indicates that segmentation effect of the improved ACM model is improved compared with those of the KI, Otsu, and PSSC-DRLSE, but the oil spill edge is partly blurred due to over-smoothing. The BF-DRLSE algorithm achieves the best performance with accurate boundaries for oil spill regions, and there are only a few mis-segmented areas.

Fig.9 Visual segmentation results for oil spills based on areas of interest (AOI) a. DRLSE; b. DRLSE after BF; c. Otsu; d. KI; e. BF-DRLSRE; f. PSSC-DRLSE; g. improved ACM; h. MRF-LSM; i. interpretation image.

The accuracy of the proposed algorithm and the other methods was manually evaluated by comparing the identified oil spill with the interpretation image. Specifically, the accuracy was determined based on the overall accuracy (OA) and kappa coefficient. For these metrics, large values reflect accurate classification.

The kappa equation can be formulated as:

where r is the number of rows in the error matrix; xii is the number of observations along the diagonal of the error matrix; xi+ and x+i are the marginal sums of row i and column i, respectively; and N is the total number of observations.

OA is computed as follows:

where xjj is the number of correctly segmented pixels. To evaluate the performances of different methods, this paper uses the confusion matrix and Kappa statistics to quantitatively analyze the experimental results of the BF-DRLSE model. Tables 6-8 give the OA and kappa coefficients of the eight methods. Except for the Otsu and KI methods, the overall accuracies of the classification methods are above 71%, and the kappa coefficients are greater than 0.70. Compared with the other methods, BF-DRLSE shows a significant improvement in the overall classification accuracy and kappa coefficient. Furthermore, the recall, precision, and F-measure of the experimental results based on BF-DRLSE are mostly better than those based on the other methods, which shows that BF-DRLSE can obtain oil spill classification results that are better than those of other methods. The results of these three groups of experiments confirm that BF-DRLSE is more suitable for oil spill classification than those of other methods are.

Table 6 Accuracy assessment of AOI1 classification results based on confusion matrix
Table 7 Accuracy assessment of AOI2 classification results based on confusion matrix
Table 8 Accuracy assessment of AOI3 classification results based on confusion matrix

The confusion matrix of the oil-spill classification results using the BF-DRLSE algorithm is reported in Table 9. According to the confusion matrix, classification error was caused mainly by the misclassification of water as oil.

Table 9 Confusion matrix of AOIs using BF-DRLSE

In this study, an advanced segmentation framework BF-DRLSE is proposed. The proposed BF-DRLSE segmentation framework can more accurately find the boundaries between seawater and oil spills from SAR images having speckle noise and blurred boundaries. The main contributions of segmentation framework BF-DRLSE are summarized below.

(1) The accurate segmentation of oil-spill edges is crucial for extracting oil-spill information. A bilateral filtering scheme is incorporated into the energy function during the level set evolution process, and it is capable of smoothing SAR images without blurring edges by minimizing the energy. The noise is filtered and the oil-spill edges are maintained, indicating that the selected bilateral filtering scheme improves the accuracy of oil-spill segmentation over traditional methods.

(2) The effectiveness of the improved BF-DRLSE algorithm with an optimal initial box boundary was validated, and satisfactory experimental results were obtained. The results reveal that optimized initial box boundaries were helpful in approximating the feature boundaries of the area of interest. This approach not only decreased the computational time but also improved the classification accuracy.

(3) The combined usage of the optimal initial box boundary and the BF-DRLSE algorithm improved segmentation performance, thereby verifying the effectiveness and applicability of the proposed framework for marine oil spill segmentation.


The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.


The authors thank the reviewers and editors for their professional and diligent works.

Adler D C, Ko T H, Fujimoto J G. 2004. Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter. Optics Letters, 29(24): 2 878-2 880. DOI:10.1364/OL.29.002878
Alves T M, Kokinou E, Zodiatis G, Radhakrishnan H, Panagiotakis C, Lardner R. 2016. Multidisciplinary oil spill modeling to protect coastal communities and the environment of the Eastern Mediterranean Sea. Scientific Reports, 6: 36882. DOI:10.1038/srep36882
Argenti F, Lapini A, Bianchi T, Alparone L. 2013. A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geoscience and Remote Sensing Magazine, 1(3): 6-35. DOI:10.1109/MGRS.2013.2277512
Cheng Y C, Li X F, Xu Q, Garcia-Pineda O, Andersen O B, Pichel W G. 2011. SAR observation and model tracking of an oil spill event in coastal waters. Marine Pollution Bulletin, 62(2): 350-363. DOI:10.1016/j.marpolbul.2010.10.005
Chiau W Y. 2005. Changes in the marine pollution management system in response to the Amorgos oil spill in Taiwan. Marine Pollution Bulletin, 51(8-12): 1 041-1 047. DOI:10.1016/j.marpolbul.2005.02.048
Cisneros-Montemayor A M, Sumaila U R. 2010. A global estimate of benefits from ecosystem-based marine recreation: potential impacts and implications for management. Journal of Bioeconomics, 12(3): 245-268. DOI:10.1007/s10818-010-9092-7
de Andrade M L S C, Skeika E, Aires S B K. 2017. Segmentation of the prostate gland in images using prior knowledge and level set method. In: 2017 Workshop of Computer Vision (WVC). IEEE, Natal, Brazil. p. 31-36.
Espeseth M M, Brekke C, Jones C E, Holt B, Freeman A. 2020. The impact of system noise in polarimetric SAR imagery on oil spill observations. IEEE Transactions on Geoscience and Remote Sensing, 58(6): 4 194-4 214. DOI:10.1109/TGRS.2019.2961684
Fawwaz I, Zarlis M, Suherman, Rahmat R F. 2018. The edge detection enhancement on satellite image using bilateral filter. In: The 10th International Conference Numerical Analysis in Engineering. IOP, Banda Aceh, Indonesia. p. 012052,
Ganta R R, Zaheeruddin S, Baddiri N, Rao R R. 2012. Segmentation of oil spill images with illumination-reflectance based adaptive level set model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5): 1 394-1 402. DOI:10.1109/JSTARS.2012.2201249
Gautama B G, Longépé N, Fablet R, Mercier G. 2016. Assimilative 2-D Lagrangian transport model for the estimation of oil leakage parameters from SAR images: application to the Montara oil spill. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11): 4 962-4 969. DOI:10.1109/JSTARS.2016.2606110
Jackson C R, Apel J R. 2004. Synthetic Aperture Radar: Marine User's Manual. 457p. Accessed on 2020-02-24.
Jubai A, Jing B, Yang J. 2006. Combining fuzzy theory and a genetic algorithm for satellite image edge detection. International Journal of Remote Sensing, 27(14): 3 013-3 024. DOI:10.1080/01431160600554371
Karantzalos K, Argialas D. 2008. Automatic detection and tracking of oil spills in SAR imagery with level set segmentation. International Journal of Remote Sensing, 29(21): 6 281-6 296. DOI:10.1080/01431160802175488
Li C M, Xu C Y, Gui C F, Fox M D. 2010. Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on Image Processing, 19(12): 3 243-3 254. DOI:10.1109/TIP.2010.2069690
Li Y, Zhang Y Z, Chen J, Zhang H S. 2014. Improved compact polarimetric SAR quad-pol reconstruction algorithm for oil spill detection. IEEE Geoscience and Remote Sensing Letters, 11(6): 1 139-1 142. DOI:10.1109/lgrs.2013.2288336
Liu J Y, Zhang Z N, Yang H M. 2015. A variational level set remote sensing SAR image segmentation approach for oil spill detecting based on fuzzy cluster. Applied Mechanics and Materials, 719-720: 1 049-1 055. DOI:10.4028/
Osher S, Sethian J A. 1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1): 12-49. DOI:10.1016/0021-9991(88)90002-2
Rischard J F. 2001. High noon: we need new approaches to global problem-solving, fast. Journal of International Economic Law, 4(3): 507-525. DOI:10.1093/jiel/4.3.507
Routray S, Ray A K, Mishra C. 2018. Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform. Optik, 159: 333-343. DOI:10.1016/j.ijleo.2018.01.096
Sethian J A. 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd edn. Cambridge University Press, Cambridge, UK. 378p.
Shao Z, Zhai H Y, Liu X Y. 2013. Segmentation of oil spill images based on SFCM and level set methods. Journal of Changchun University of Science and Technology (Natural Science Edition), 36(3-4): 134-137. (in Chinese with English abstract) DOI:10.3969/j.issn.1672-9870.2013.03.040
Solberg A H S. 2012. Remote sensing of ocean oil-spill pollution. Proceedings of the IEEE, 100(10): 2 931-2 945. DOI:10.1109/JPROC.2012.2196250
Song D M, Wang B, Chen W M, Wang N, Yu S Y, Ding Y, Liu B, Zhen Z J, Xu M M, Zhang T. 2018. An efficient marine oil spillage identification scheme based on an improved active contour model using fully polarimetric SAR imagery. IEEE Access, 6: 67 959-67 981. DOI:10.1109/access.2018.2876173
Song H H, Huang B, Zhang K H. 2013. A globally statistical active contour model for segmentation of oil slick in SAR imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(6): 2 402-2 409. DOI:10.1109/JSTARS.2013.2255119
Sussman M, Fatemi E. 1999. An efficient, interface-preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM Journal on Scientific Computing, 20(4): 1 165-1 191. DOI:10.1137/S1064827596298245
Sussman M, Smereka P, Osher S. 1994. A level set approach for computing solutions to incompressible two-phase flow. Journal of Computational Physics, 114(1): 146-159. DOI:10.1006/jcph.1994.1155
Tang L L, Jiang P, Dai C D, Jackson T J. 1996. Evaluation of smoothing filters suppressing speckle noise on SAR images. Remote Sensing of Environment China, 11(3): 206-211. (in Chinese with English abstract)
Tomasi C, Manduchi R. 1998. Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision. IEEE, Bombay, India. p. 839-846.
Topouzelis K, Psyllos A. 2012. Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS Journal of Photogrammetry and Remote Sensing, 68: 135-143. DOI:10.1016/j.isprsjprs.2012.01.005
Wang W D, Sheng H, Liu S W, Chen Y L, Wan J H, Mao J J. 2019. An edge-preserving active contour model with bilateral filter based on hyperspectral image spectral information for oil spill segmentation. In: 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, Amsterdam, Netherlands. p.1-5,
Wu Y F, He C J, Yang L, Su M T. 2017. A backscattering-suppression-based variational level-set method for segmentation of SAR oil slick images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12): 5 485-5 494. DOI:10.1109/JSTARS.2017.2740979
Xu B, Cui Y, Zuo B, Yang J, Song J S. 2016. Polarimetric SAR image filtering based on patch ordering and simultaneous sparse coding. IEEE Transactions on Geoscience and Remote Sensing, 54(7): 4 079-4 093. DOI:10.1109/TGRS.2016.2536648
Yang X, Gao X B, Li J, Han B. 2014. A shape-initialized and intensity-adaptive level set method for auroral oval segmentation. Information Sciences, 277: 794-807. DOI:10.1016/j.ins.2014.03.014
Yang X, Gao X B, Tao D C, Li X L, Li J. 2015. An efficient MRF embedded level set method for image segmentation. IEEE Transactions on Image Processing, 24(1): 9-21. DOI:10.1109/TIP.2014.2372615
Zhang G S, Perrie W, Zhang B, Khurshid S, Warner K. 2018. Semi-empirical ocean surface model for compact-polarimetry mode SAR of RADARSAT Constellation Mission. Remote Sensing of Environment, 217: 52-60. DOI:10.1016/j.rse.2018.08.006
Zhang K H, Zhang L, Song H H, Zhang D. 2013. Reinitialization-free level set evolution via reaction diffusion. IEEE Transactions on Image Processing, 22(1): 258-271. DOI:10.1109/TIP.2012.2214046
Zhang T, Lü X L, Qian J, Hong J, Li Y. 2016. Bilateral linear SURE-based SAR interferogram filter. Remote Sensing Letters, 7(12): 1 190-1 198. DOI:10.1080/2150704x.2016.1225169