基于合作模型的SAR图像细长油膜带分割方法

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"Elongated Strip Oil Spill Segmentation Based on A Cooperative Model" 本文提出了一种基于合作模型的新型框架，用于对SAR（合成孔径雷达）图像中的细长条状海洋油污进行分割。SAR图像由于其包含大量噪声，传统图像分割方法如阈值分割在处理这类图像时往往效果不理想，特别是在识别海洋中的细长油膜带时。合作模型是一种基于能量函数最小化的图像分割方法，特别适用于解决此类问题。在合作模型中，数据一致性通过有限伽马混合模型来建模，利用期望最大化（EM）算法估计模型参数。这一数据项建模方式能够更好地适应SAR图像的特性，提高分割的准确性。同时，为了刻画图像的平滑性，文中采用了高阶协作模型。这种模型不仅惩罚边界长度，还考虑到了物体边界的多样性，这使得它在分割SAR图像中的细长条状油膜区域时更具优势，能有效地抵抗噪声干扰并保持边缘的精确性。与传统的SAR图像溢油分割方法（如图割等能量最小化方法）相比，该合作模型方法表现出了更好的性能。实验结果证实了这种方法的有效性，它不仅能有效分割出细长的油膜条带，而且对噪声有较强的抗干扰能力。这为SAR图像分析和海洋环境监测提供了更准确的技术支持，尤其是在应对油污泄漏等环境紧急情况时，能够帮助快速准确地识别污染区域，以便及时采取应对措施。这项研究通过引入合作模型，创新性地解决了SAR图像中细长油膜分割的难题，提高了分割精度，为后续的环境评估和灾害响应提供了科学依据。其理论和技术对于SAR图像处理领域具有重要的理论价值和实践意义。

Elongated Strip Oil Spill Segmentation Based on A

Cooperative Model

Mengmeng Di, Huajun Song, and Peng Ren

College of Information and Control Engineering,

China University of Petroleum,

Qingdao 266580, China.

Email: dimm0616@163.com, shjharbin@163.com, pengren@upc.edu.cn

Chunbo Luo

School of Computing,

University of The West of Scotland,

Paisley PA1 2BE, UK.

Email: chunbo.luo@uws.ac.uk

Abstract—This paper describes a novel framework to segment

elongated strips of marine oil spill based on a cooperative model.

We construct a higher-order energy function which characterizes

data consistency and smoothness of varying orders in an inte-

grated framework. We model the data consistency in terms of a

ﬁnite Gamma mixture and then estimate its parameter values

using an expectation maximization (EM) algorithm. In order

to characterise the smoothness term we exploit a cooperative

model which not only penalizes the length but also addresses

the diversity of the object boundary. This advantage makes our

framework more effective for segmenting elongated strip oil spill

than traditional energy minimization methods such as graph cuts.

Our framework is further evaluated using practical SAR images

of oil spill recordings and its effectiveness is validated.

Keywords—Cooperative model, Oil spill segmentation, Graph

cuts.

I. INTRODUCTION

The global economy relies heavily on oil resources and

oil spill accidents from oil drilling platforms, ships or pipes

have occurred frequently. Oil spilled to marine environment

can cause severe harms to the natural environment and local

ecosystem and damage the aquaculture and economy [1].

Therefore, early detection of oil spill on marine surface is

particularly important. Of all the monitoring measures, Syn-

thetic Aperture Radar (SAR) installed on satellites have the

advantages of all-weather and all-time operation [2] which

make it a desirable means for detecting and monitoring oil

spilling [3].

In order to effectively detect and recognize oil spill on

ocean surface, the segmentation of oil spill areas in SAR

images is highly required and has been extensively studied

[4][5]. According to the principles of SAR imaging, the grey

values within one area of the SAR image may vary slightly

if the associated regions in the scene have similar reﬂection

characteristics and scattering properties. On the other hand,

different real world textures exhibit different grey values in

the SAR image. As a result, the grey level distribution of

a SAR image is rather complex and the oil spill image

segmentation based on such distribution becomes a difﬁcult

task. In addition, weather conditions can change the marine

surface and reﬂection, and thus make oil spill segmentation be

more complicated.

In research literature, generic image segmentation methods

have been widely applied for oil spill segmentation [6]. One

straightforward way is to set a threshold for the grey values and

segment the SAR image into oil areas and non-oil areas [7][8].

This pixel-wise strategy is efﬁcient in implementation, but

it neglects the pairwise relationship between the neighboring

pixels and may cause ambiguity if the noise level is high which

is usually the case for SAR imaging. Another category of

commonly used graph-cuts based methods combine pixel and

pairwise penalties together to yield more robust segmentation

schemes for processing generic images. However, SAR images

with oil spills are different from generic images for the fact

that there are a considerable amount of oil spill areas exposed

with the shape of elongated strips. In this case, graph cuts

[9] based methods tend to be much ineffective because the

pairwise terms in graph cuts encourage smooth segmentations

by penalizing the assignment of different labels to neighbor-

ing pixels, therefore, the graph cuts cannot characterize the

boundaries of elongated strips properly.

To address the drawbacks of the existing methods, we

exploit a cooperative model [10] which has been recently

proposed by Pushmeet Kohli, to model the higher-order terms

of the energy function for oil spill segmentation. Instead of

favouring short oil boundaries by penalizing the number of

label discontinuities, our method focuses on the congruous

boundaries by penalizing the number of types of label dis-

continuities and thus has the advantages of preserving the

boundaries of elongated strips and improved performance. The

experiment results conﬁrm that our method is more effective

in segmenting the elongated stripped oil spills of SAR images

than existing methods such as thresholding and graph cuts.

II. ENERGY FORMULATION

We formulate the problem of oil spill segmentation in terms

of an energy minimization scheme. The ﬁrst step is to construct

the energy function for segmentation. We use the set P =

, p

, ..., p

} to denote the pixel set of a SAR image, and I

to denote the pixel gray value in set P . The object of oil spill

segmentation is to assign each pixel p

∈ P a binary value

∈ {0, 1}, where the label 0 represents an oil ﬁlling area

and the label 1 represents a background area. The set of all

binary variables x

for p

is denoted by X.

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