Analysis of Scattering Properties of Continuous Slow-Release Slicks on the Sea Surface Based on Polarimetric Synthetic Aperture Radar

Li, Guannan; Li, Ying; Liu, Bing-Xin; Hou, Yongchao; Fan, Jianchao

doi:10.3390/ijgi7070237

Cited by 14 publications

(16 citation statements)

References 42 publications

(54 reference statements)

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“…In the case of polarimetric SAR oil spill detection, the returns from the oil slick and look-alike regions may be masked by sensor instrument noise for their backscatter signal may be lower than the sensor noise floor, i.e., Noise Equivalent Sigma Zero (NESZ). The NESZ should be lower than the returns to ensure that the information of each polarization channel is not interfered [46,47]. In such a situation, the self-similarity parameter and other polarimetric features which using full polarization information can accurately describe marine target and be used for oil spill detection.…”

Section: Noise Analysismentioning

confidence: 99%

Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

et al. 2019

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Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

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Section: Noise Analysismentioning

confidence: 99%

Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

et al. 2019

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“…The oceans play an important role in the global ecosystem, as they affect the global ecological balance and provide resources and energy. Pollution of the ocean surface by mineral or petroleum oil is a major environmental problem [1,2]. The main causes of marine floating oil slicks can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…The first category includes oil leakage as a result of human activity such as shipping accidents, local leakage near ports, and oil well explosions. The second includes fairly slow and relatively constant natural seepage of oil from the seabed [2,3,4]. As much as half the oil that enters the coastal environments come from natural oil and gas seeps.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with conventional ground-based monitoring techniques, remote sensing is an effective way to monitor, track, and map targets at a regional scale due to its wide synchronous coverage and cost effectiveness [8]. As a tool that can be used at all times of day and in all weather conditions, polarimetric synthetic aperture radar (Pol-SAR) can provide valuable information using its own active illumination systems and multiple imaging and polarization modes [2,9,10,11]. This increases the range of applications for Pol-SAR data and means that effective information can be retrieved from targets with different characteristics and for various research demands.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, most methods of these studies are applied to oil spill images obtained from different sensors, different times, and different sea areas. The difference of SAR sensors designs and detection conditions may affect the effectiveness and classification accuracy of the method [2,33]. Here, brief notes and inspiration from the work in spectral similarity measure based on SAR images [8,26], as well as an improvement and enhancement to forms the background to the present study.…”

Section: Introductionmentioning

confidence: 99%

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Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Liu

et al. 2019

Sensors

Self Cite

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Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.

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Experimental Analysis on the Ultraviolet Imaging of Oil Film on Water Surface: Implication for the Optimal Band for Oil Film Detection Using Ultraviolet Imaging

Xie

2022

Arch Environ Contam Toxicol

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Analysis of Scattering Properties of Continuous Slow-Release Slicks on the Sea Surface Based on Polarimetric Synthetic Aperture Radar

Cited by 14 publications

References 42 publications

Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Experimental Analysis on the Ultraviolet Imaging of Oil Film on Water Surface: Implication for the Optimal Band for Oil Film Detection Using Ultraviolet Imaging

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