A Statistical Approach to Preprocess and Enhance C-Band SAR Images in Order to Detect Automatically Marine Oil Slicks

Najoui, Zhour; Riazanoff, Serge; Deffontaines, Benoı̂t; Xavier, Jean-Paul

doi:10.1109/tgrs.2017.2760516

Cited by 12 publications

(10 citation statements)

References 28 publications

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“…The pattern in the image looks like a viscous flow in the sea area then gets thinner towards the coast. This pattern is similar to the classification of dark patches for mineral categories [23]. The oil comes from seepage from the seabed or anthropogenic oil spills from ships, factories, oil refineries, and oil platforms.…”

Section: Discussionsupporting

confidence: 76%

The capability of Sentinel-1 polarization combinations for oil spill detection (study case: Karawang, Indonesia)

Afgatiani

Suhadha

Ibrahim

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Marine pollution is one of issues that is widely discussed such as marine debris, chemical contamination, and oil spill. It has many impacts on the environment. Remote sensing helps detect the presence of an oil spill via satellite platform. One commonly used satellite is Synthetic-Aperture Radar (SAR) imagery, which can see oil spills even in cloudy conditions. However, the black and white appearance of the radar sometimes makes it difficult to distinguish between an oil spill and look alike. Therefore, this study aims to determine the polarization combination in the Sentinel-1 SAR imagery. Sentinel-1 Single Look Complex (SLC) is used as input and processes through various stages to obtain Coherence Vertical-Vertical (VV) and Vertical-Horizontal (VH) results. There are four steps for processing the data; convert the data to backscattering coefficient from SLC data, stacking, Terrain Observation by Progressive Scan (TOPS) coherence, and building the robust combinations. The polarization in the Sentinel-1 was calculated to determine artificial bands for the correct combination, namely Divide (VV/VH) as band 1, Average ((VV+VH)/2) as band 2, Multiple (VVxVH) as band 3, Add (VV+VH) as band 4, and Subtract (VV-VH) as band 5. Furthermore, the determination of the band used in the polarization composite display is based on the intensity value. As a result, the recommendation for Sentinel-1 polarization is obtained, which involves bands 2, 3, and 5.

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Section: Discussionsupporting

confidence: 76%

The capability of Sentinel-1 polarization combinations for oil spill detection (study case: Karawang, Indonesia)

Afgatiani

Suhadha

Ibrahim

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…The applied preprocessing consists of applying local stretching with an average of 140 and a standard deviation of 60 on a 301-pixel sliding window in order to optimize the detectability of the oil slicks (Fig. 4; Najoui, 2017;Najoui et al, 2018a).…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Non-oil-related dark patches are termed "look-alike features" and include upwelling, eddies, rainfalls, wind shadows, and internal waves, among others. (Brekke and Solberg, 2005;Espedal, 1999;Najoui et al, 2018a;Xu et al, 2015). These non-oil-related features are mostly due to meteorological conditions.…”

Section: Manual Detectionmentioning

confidence: 99%

Oil slicks in the Gulf of Guinea – 10 years of Envisat Advanced Synthetic Aperture Radar observations

Najoui¹,

Amoussou

Riazanoff

et al. 2022

Earth Syst. Sci. Data

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Abstract. The Gulf of Guinea is a very active area with respect to maritime traffic as well as oil and gas exploitation. Due to the failure of some actors to comply with environmental standards, this region has been subject to a large number of oil pollution episodes. This anthropogenic oil pollution is in addition to natural oil seepage from the ocean floor. This study aims to detect oil slicks in the Gulf of Guinea and to analyse their spatial distribution using synthetic-aperture radar (SAR) images. Previous works have already locally mapped oil slicks in this area; however, to our knowledge, this study is the first to achieve a global statistical analysis based on 10 years of radar images covering 17 exclusive economic zones (EEZs) in the Gulf of Guinea. The present study is based on a database of 3644 SAR images collected between 2002 and 2012 by the Advanced Synthetic Aperture Radar (ASAR) sensor onboard the European Spatial Agency (ESA) Envisat mission, and these images allowed the identification of 18 063 oil slicks. The spatial distribution of these oil slicks is available from Zenodo: https://doi.org/10.5281/zenodo.6470470 (Najoui, 2022b). The oil slicks detected in this work encompass both oil spills of anthropogenic origin and oil seeps of natural origin (natural oil reservoir leaks). A set of 100 georeferenced oil spills is also available from Zenodo: https://doi.org/10.5281/zenodo.6907743 (Najoui, 2022a).

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“…Unluckily, several ocean phenomena and interfering substances can dampen the Bragg waves and produce low backscattering areas. They appear as dark patches (false targets) in SAR imagery, which are called lookalikes (Najoui et al 2018).…”

Section: Challenges Of Utilizing Sar Datamentioning

confidence: 99%

“…The former, also called surfactants, is surface films that contain surface-active organic compounds produced by marine plants (e.g., planktons) or animals (e.g., fish) or they are floating macro-algae such as sargassum and kelp (Najoui et al 2018;Minchew, Jones, and Holt 2012). The latter contains two subcategories, including natural oil seeps that stem from sea bottom petroleum reservoirs (crude oil) and anthropogenic oil spills that discharged and leaked from ships and platforms, oil terminals, processing of industrial or urban plants (e.g., sewage plants), oil pipelines, and refineries (Najoui et al 2018;Espedal and Johannessen 2000). It is important to note that the focus of this paper is to review the studies and advances to address the monitoring of mineral oil spills.…”

Section: Introductionmentioning

confidence: 99%

Oil spill detection from Synthetic Aperture Radar Earth observations: a meta-analysis and comprehensive review

Jafarzadeh

Mahdianpari

Homayouni

et al. 2021

GIScience & Remote Sensing

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A Statistical Approach to Preprocess and Enhance C-Band SAR Images in Order to Detect Automatically Marine Oil Slicks

Cited by 12 publications

References 28 publications

The capability of Sentinel-1 polarization combinations for oil spill detection (study case: Karawang, Indonesia)

The capability of Sentinel-1 polarization combinations for oil spill detection (study case: Karawang, Indonesia)

Oil slicks in the Gulf of Guinea – 10 years of Envisat Advanced Synthetic Aperture Radar observations

Oil spill detection from Synthetic Aperture Radar Earth observations: a meta-analysis and comprehensive review

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