scholarly journals Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

2020 ◽  
Vol 12 (20) ◽  
pp. 3416
Author(s):  
Shamsudeen Temitope Yekeen ◽  
Abdul-Lateef Balogun
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Oil Spill ◽  
Oil Spills ◽  
Biogenic Elements ◽  
Trajectory Prediction ◽  
Oil Spill Detection ◽  
Remote Sensing Technology ◽  
Sensing Technology

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

scholarly journals Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology

2019 ◽  
Vol 8 (4) ◽  
pp. 181 ◽  
Author(s):  
Xueyuan Zhu ◽  
Ying Li ◽  
Qiang Zhang ◽  
Bingxin Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Film Thickness ◽  
Oil Spill ◽  
Spectral Characteristics ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Oil Film ◽  
Remote Sensing Technology ◽  
Sensing Technology

Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified.

scholarly journals Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

2021 ◽  
Vol 14 (1) ◽  
pp. 157
Author(s):  
Zongchen Jiang ◽  
Jie Zhang ◽  
Yi Ma ◽  
Xingpeng Mao
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Heavy Oils ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Moment Estimation ◽  
Marine Oil Spills ◽  
Oil Films

Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

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

2020 ◽  
Vol 12 (20) ◽  
pp. 3338
Author(s):  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Abdallah Shanableh ◽  
Abubakir Kais ◽  
Osman Hamed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Oil Spill ◽  
State Of The Art ◽  
Remotely Sensed ◽  
Classification Systems ◽  
Remotely Sensed Data ◽  
Future Directions ◽  
Oil Spill Detection ◽  
Different Sources

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.

scholarly journals Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning

2020 ◽  
Vol 12 (14) ◽  
pp. 2260 ◽  
Author(s):  
Filippo Maria Bianchi ◽  
Martine M. Espeseth ◽  
Njål Borch
Keyword(s):  
Deep Learning ◽  
Oil Spill ◽  
Large Scale ◽  
Oil Spills ◽  
Sar Images ◽  
Oil Spill Detection ◽  
Learning Framework ◽  
Large Scale Data ◽  
Human Operators ◽  
Scale Detection

We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories of its shape and texture characteristics. The classification results provide valuable insights for improving the design of services for oil spill monitoring by world-leading providers. Finally, we present our operational pipeline and a visualization tool for large-scale data, which allows detection and analysis of the historical occurrence of oil spills worldwide.

OIL SPILL DETECTION WITH REMOTE SENSORS

2016 ◽  
Author(s):  
Kristina Pilžis ◽  
Vaidotas Vaišis
Keyword(s):  
Remote Sensing ◽  
Surface Properties ◽  
Oil Spill ◽  
Marine Environment ◽  
Oil Spills ◽  
Sea Surface ◽  
Optical Remote Sensing ◽  
Oil Spill Detection ◽  
Remote Sensors ◽  
Microwave Sensors

Accurate detection and forecasting of oil spills and their trajectories is beneficial for monitoring and conservation of the marine environment. The most common techniques of oil spill tracking are remote sensing from an aircraft and satellites. Remote sensors work by detecting sea surface properties and the most effective of them are laser fluorosensors, optical remote sensing (visible, infrared, ultraviolet) and microwave sensors. Possibilities and advantages of their use are reviewed in this article.

scholarly journals Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

2019 ◽  
Vol 11 (7) ◽  
pp. 756 ◽  
Author(s):  
Peng Liu ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Chen ◽  
and Jin Xu
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection Method ◽  
Adaptive Thresholding ◽  
Oil Spill Detection ◽  
Radar Images ◽  
X Band ◽  
Marine Radar

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

Primary Studies on the Offshore Oil Spill Detection System Using the Satellite Remote Sensing Technology Developed by China National Offshore Oil Corporation

2013 ◽  
Vol 316-317 ◽  
pp. 580-585 ◽  
Author(s):  
Jian Liu ◽  
Sheng Feng Zhu
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Satellite Remote Sensing ◽  
Detection System ◽  
Rapid Development ◽  
Oil Spill Detection ◽  
Remote Sensing Technique ◽  
Detection Systems ◽  
Sensing Technology ◽  
Offshore Oil

Oil spill detection has important significance for the oceanic environmental protection. With the rapid development of the satellite remote sensing, remote sensing technique has become one of the important and effective tools in oil spill detection. This paper discussed the method of the offshore surface oil spill detection using Synthetic Aperture Radar (SAR). The oil spill detection systems used at home and abroad is evaluated. Finally, the feasibility of the oil spill detection system based on the satellite remote sensing developed by China National Offshore Oil Corporation is studied.

scholarly journals APPLICATION OF ACTIVE REMOTE SENSING METHODS FOR OIL SPILL DETECTION

2021 ◽  
Vol 6 ◽  
pp. 213-218
Author(s):  
Pavel I. Nazdrachev ◽  
Alexander Yu. Chermoshentsev
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Oil Spills ◽  
Oil Spill Detection ◽  
Radar Images ◽  
Remote Sensing Methods

The article describes the implementation of the method for processing radar images from the Sentinel-1 satellite on the territory of the Sakhalin Region, the purpose of which is to detect oil spills. The possibility of using this technique for the prompt detection of oil spills in water areas, as well as for monitoring is shown.

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