Oil spill remote sensing: a forensics approach

Fingas, Merv; Brown, Carl E.

doi:10.1016/b978-0-12-803832-1.00021-0

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…Given the wide coverage, synoptic views, and the frequency of acquiring multisensory data, satellite-based remote sensing (RS) has been extensively used in detecting and monitoring oil spills in the past few decades using various types of remotely sensed data (Section 2). Numerous review papers that focus on oil spill RS are available in the literature [13][14][15][16][17][18]. These studies discussed the utilization and advantages of using various sources of remotely sensed data and techniques for oil spill extraction.…”

Section: Introductionmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Section: Introductionmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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“…The monitoring and detection of oil slicks is the main component of oil spill emergency management decision support [4]. Traditional methods of oil spill monitoring use aerial photography or field investigations, which require large amounts of manpower and material resources and have poor timeliness [5].…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

et al. 2022

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Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.

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Spectral characteristics of hydrocarbon-bearing materials in Karamay, northwest China: implications for remote detection of onshore oil seeps

Qiu

Yan

et al. 2020

Spectroscopy Letters

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Oil spill remote sensing: a forensics approach

Cited by 4 publications

References 51 publications

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

Spectral characteristics of hydrocarbon-bearing materials in Karamay, northwest China: implications for remote detection of onshore oil seeps

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