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Oil spill detection based on compact polarimetric eigenvalue decomposition

  • role： First author第一作者
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

  • Email：xie_gq@163.com
  • Introduction：谢广奇，1992年生，男，硕士研究生，研究方向为极化SAR海洋目标提取。E-mail：xie_gq@163.com
Guangqi XIE,  
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Shuai YANG,  
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Qihao CHEN,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

  • Email：liuxg318@163.com
  • Introduction：刘修国，1969年生，男，教授，研究方向为遥感影像信息提取与3S集成。E-mail：liuxg318@163.com
Xiuguo LIU*

resumen

Oil spill is one of the most common causes of marine pollution. Synthetic aperture radar (SAR) can detect marine oil spills due to its all-weather and all-day imaging capability. However, conventional algorithms using single-channel SAR usually confuses oil spills with oil spill lookalikes, making oil spill detection a challenging task. In recent years, polarimetric SAR has been studied to improve oil spill detection performance, particularly with respect to distinguishing oil spills and lookalikes. Compact polarimetric (CP) SAR is one of the most popular trends of polarimetric SAR platforms. Compared with full polarimetric SAR, CP SAR contains abundant polarization information of scattering targets. CP SAR also has large imaging range and its manufacturing cost is considerably low. Therefore, CP SAR is a promising tool for oil spill detection. In this study, we utilized CP HP-mode data and derived three parameters to distinguish oil spills from lookalikes. The three novel parameters are CP entropy (Hc), CP fraction (PFc), and CP pedestal height (PHc), which are conducted from CP eigenvalue decomposition. The scattering type of sea surface is dominated by low entropy scattering. For instance, Bragg scattering from small rough surfaces can be regarded as weakly depolarized. In this vein, CP eigenvalue $\hat I {\gg 1}$ is much higher than $\hat I {\gg 2}$ , which indicates the dominance of Bragg scattering. On the contrary, oil spill weakens the Bragg scattering of the ocean, and the difference between the two eigenvalues is not as high as before. Based on former consideration, Hc is high, PFc is low, and PHc is high, which means that the average scattering is in a depolarized state. The target is considered “random”, and polarization status information is lost. Hence, the aforementioned parameters can be used to detect oil spills. The threshold is determined by Otsu’s method and statistical analysis. The threshold for PFc and PHc was automatically determined by Otsu’s method, and other parameters were statistically analyzed to determine the threshold. In this study, Radarsat-2 and SIR-C/X-SAR data of C-band were used to detect oil spills. The results show that Hc, PFc, and PHc can effectively distinguish oil spills and lookalikes. The performance of all the parameters based on the CP eigenvalue decomposition is excellent in effectively excluding lookalikes, highlighting oil spill areas, and enhancing differences between oil spill and sea clutter. Moreover, the parameters are excellent in effectively maintaining details. CP SAR is promising in polarimetric SAR system because of its ability to detect a wide range of marine oil spills. The parameters derived in this study, which were based on the CP eigenvalue decomposition, can obtain satisfactory results in distinguishing oil spills from lookalikes. The quantitative experiments also confirm that the Bhattacharyya distance between oil spills and sea surface surpasses that of lookalikes, which proves that our method achieves fine oil spill detection.

palabra clave

oil spill detection;compact polarimetric SAR;eigenvalue decomposition;compact polarimetric parameter;Bragg scattering

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