Oil Spill Detection Research Articles

Application of Sentinel 1 imagery data to detect and classify oil spills on the ocean

Sentinel is the name of a series of Earth observation missions (from Sentinel 1 to Sentinel 6) developed by the Copernicus initiative and operated by the European Space Agency (ESA). Sentinel satellite image data, which includes optical and radar images, provided completely free of charge, has been widely and effectively used in Earth research. The paper presents a technical solution using Sentinel 1 satellite image in detecting and monitoring oil spill pollution at sea, testing for Mauritius sea area. The Otsu automatic thresholding method was applied to extract oil spills at sea from Sentinel 1A radar images. The processing was done on the Google Earth Engine (GEE) cloud computing platform. The results of the study contribute to improving the efficiency of the application of radar remote sensing data in early detection and classification of oil spills, supporting the response to oil spill pollution at sea.

Design, Modeling, and Visual Learning-Based Control of Soft Robotic Fish Driven by Super-Coiled Polymers.

A rapidly growing field of aquatic bio-inspired soft robotics takes advantage of the underwater animals’ bio-mechanisms, where its applications are foreseen in a vast domain such as underwater exploration, environmental monitoring, search and rescue, oil-spill detection, etc. Improved maneuverability and locomotion of such robots call for designs with higher level of biomimicry, reduced order of complex modeling due to continuum elastic dynamics, and challenging robust nonlinear controllers. This paper presents a novel design of a soft robotic fish actively actuated by a newly developed kind of artificial muscles—super-coiled polymers (SCP) and passively propelled by a caudal fin. Besides SCP exhibiting several advantages in terms of flexibility, cost and fabrication duration, this design benefits from the SCP’s significantly quicker recovery due to water-based cooling. The soft robotic fish is approximated as a 3-link representation and mathematically modeled from its geometric and dynamic perspectives to constitute the combined system dynamics of the SCP actuators and hydrodynamics of the fish, thus realizing two-dimensional fish-swimming motion. The nonlinear dynamic model of the SCP driven soft robotic fish, ignoring uncertainties and unmodeled dynamics, necessitates the development of robust/intelligent control which serves as the motivation to not only mimic the bio-mechanisms, but also mimic the cognitive abilities of a real fish. Therefore, a learning-based control design is proposed to meet the yaw control objective and study its performance in path following via various swimming patterns. The proposed learning-based control design employs the use of deep-deterministic policy gradient (DDPG) reinforcement learning algorithm to train the agent. To overcome the limitations of sensing the soft robotic fish’s states by designing complex embedded sensors, overhead image-based observations are generated and input to convolutional neural networks (CNNs) to deduce the curvature dynamics of the soft robot. A linear quadratic regulator (LQR) based multi-objective reward is proposed to reinforce the learning feedback of the agent during training. The DDPG-based control design is simulated and the corresponding results are presented.

GreyWolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery

ABSTRACT Oil spill detection (OSD) in marine areas is an application of synthetic aperture radar (SAR) images to protect aquatic life. In this paper, a new oil spill detection algorithm based on level set method (LSM) is presented. Dark spot detection, feature extraction, and classification are the main steps in the proposed method. In the first step, a new dark spot detection method in SAR images is presented, which introduces a combination of multi-objective grey wolf optimization (MOGWO) and K-means clustering to find the best threshold level for image segmentation. This method overcomes the K-means clustering by choosing optimal number of clusters and centers. In the feature extraction step, a vector consisting of 45 arrays of calculated Legendre moments in the main, gradient and radon transform of the image is used. The oil-suspicious areas are also discriminated by the supported vector machine (SVM) classifier, and their boundaries are applied as an initial contour to a new hierarchical region-based level-set method (HRLSM). Experiments are performed on the images acquired by Envisat, UAVSAR, TerraSAR-X, and Sentinel1 satellites. The results showed the reliability and robustness of the proposed method with high accuracy, even for noisy images with heterogeneous and weak boundaries.

Oil-spill detection sensor using ultraviolet-induced fluorescence for routine surveillance in coastal environments

Oil-spill detection sensor using ultraviolet-induced fluorescence for routine surveillance in coastal environments

Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection

Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively.

A novel lightweight bilateral segmentation network for detecting oil spills on the sea surface

Accidental oil spills from pipelines or tankers have posed a big threat to marine life and natural resources. This paper presents a novel lightweight bilateral segmentation network for detecting oil spills on the sea surface. A novel deep-learning semantic-segmentation algorithm is firstly created for analyzing the characteristics of oil spill images. A Bilateral Segmentation Network (BiSeNetV2) is then selected as the basic network architecture and evaluated by using experimental comparison of the current mainstream networks on detection accuracy and real-time performances for oil samples. Furthermore, the Gather-and-Expansion (GE) layer of the semantic branch in the traditional network is redesigned and the parameter complexity is reduced. A dual attention mechanism is deployed in the two branches of the BiSeNetV2 to solve the problem of inter-class similarity. Finally, experimental results are given to show the good detection accuracy of the proposed network.

BO-DRNet: An Improved Deep Learning Model for Oil Spill Detection by Polarimetric Features from SAR Images

Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model’s hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively.

Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

Rapid detection of marine oil spills is becoming increasingly critical in the face of frequent marine oil spills. Oil slick thickness measurement is critical in the hazard assessment of such oil leaks. As surface plasmon resonance (SPR) sensors are sensitive to slight changes in refractive index, they can monitor offshore oil spills arising from significant differences in the refractive index between oil and water. This study presents a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor prepared by polydopamine accelerated wet chemical plating for rapid and real-time measurement of oil slick thickness. We examined oil thickness detection at two interfaces, namely, water-oil and air-oil. Detection sensitivity of −1.373%/mm is obtained at the water-oil interface in the thickness range of 0–5 mm; detection sensitivity of −2.742%/mm is obtained at the air-oil interface in the thickness range of 0–10 mm. Temperature and salinity present negligible effects on the oil slick thickness measurement. The fabricated FOSPR sensor has the ability to detect the presence of oil as well as quantify the oil thickness. It has favorable repeatability and reusability, demonstrating the significant potential for use in the estimation of marine oil slick thickness.

Toward the Detection of Oil Spills in Newly Formed Sea Ice Using C-Band Multipolarization Radar

Oil spills in the Arctic are becoming more likely as shipping traffic increases in response to climate-related sea ice loss. To improve oil spill detection capability, we used a controlled mesocosm to analyze the multipolarized C-band backscatter response of oil in newly formed sea ice (NI). Artificial sea ice was grown in two cylindrical tubs at the Sea-ice Environmental Research Facility, University of Manitoba. The sea ice physical characteristics, including surface roughness, thickness, temperature, and salinity, were measured before and after oil injection below the ice sheet. Time-series C-band radar backscatter measurements detected the differences in the sea ice evolution and oil migration to the sea ice surface in the oil-contaminated tub, which was compared to uncontaminated ice in a control tub. Immediately prior to the presence of oil on the ice surface, the copolarized backscatter is increased by 13-dB local maximum, while the cross-polarized backscatter is decreased by 9-dB. Ice physical properties suggest that the local backscatter maximum and minimum, which occurred immediately before oil migrated onto the surface, were related to a combination of brine and oil upward migration. The findings of this work provide a baseline data interpretation for oil detection in the Arctic Ocean using current and future C-band multipolarization radar satellites.

Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images

Oil spills have caused serious harm to the marine environment. Remote sensing technology is one of the important tools for marine environment monitoring. Synthetic aperture radar (SAR) has become an important technology for detecting marine pollution. Identifying dark spots is essential for oil spill detection based on SAR images. Dark spots’ detection can be achieved using image segmentation techniques. However, natural phenomena, such as waves and currents, can also cause dark spots, resulting in consistently uneven intensity, high noise, and blurred boundaries in oil spill images. In addition, existing oil spill detection models often perform well for large targets but have poor detection accuracy for small targets. To solve the above problems, the oil spill contextual and boundary-supervised detection network (CBD-Net) is proposed to extract refined oil spill regions by fusing multiscale features. To improve the internal consistency of oil spill regions, the spatial and channel squeeze excitation (scSE) block is introduced. In CBD-Net, boundary details are enhanced with optimized edge supervision. In addition, a manually labeled dataset is proposed, Deep-SAR Oil Spill (SOS) dataset, aiming to solve the problem of insufficient existing oil spill detection dataset. Experimental results demonstrate that CBD-Net outperforms other comparative models and is able to extract robust and accurate oil spill regions from complex SAR images. The highest mIoU of 83.42% and the highest F1 score of 87.87% were achieved on the SOS dataset. The CBD-Net model proposed in this article can play a guiding role in the marine oil spill decision support system.

Oil Spill Detection Based on Deep Convolutional Neural Networks Using Polarimetric Scattering Information From Sentinel-1 SAR Images

Oil spill accidents can cause severe ecological disasters; hence, the timely and effective detection of oil spills on the marine surface is of great significance. Synthetic aperture radar (SAR) is very suitable for large-scale oil spill monitoring. As a more advanced form of SAR, polarimetric SAR (PolSAR) can provide more scattering information of land objects, which can help to improve the accuracy of oil spill detection. However, the current studies of oil spill detection by SAR data have mainly focused on using SAR intensity or amplitude information, and the phase information and other polarimetric information have not been fully utilized. To solve this problem, using Sentinel-1 dual-polarimetric images as the data source, this article presents an intelligent oil spill detection architecture based on a deep convolutional neural network (DCNN), in which both the amplitude information and phase information are utilized. Furthermore, to improve the feature discrimination capability, the Cloude polarimetric decomposition parameters are also integrated into the proposed model. The results show that the improved DeepLabv3+ model, which takes ResNet-101 as the backbone network and group normalization (GN) as the normalization layer, can achieve superior performance than those traditional methods. Moreover, the model is better able to capture the fine details of oil spill instances and can achieve fine-scale segmentation.

A New Technique for Segmentation of the Oil Spills From Synthetic-Aperture Radar Images Using Convolutional Neural Network

Oil spills have proven to have detrimental effects on the marine-based environment and economy. Thus, it is necessary to identify oil spills and classify them in the sea to reduce oil-induced pollution in seas and oceans. Synthetic-aperture radar (SAR) imaging is a good option for rapid oil detection, as it covers a wide area, collects data at short intervals, and allows taking images in all weather conditions throughout the day. The reason for using deep neural networks is that training several images enhances segmentation accuracy significantly. This study intended to separate the oil spills of SAR images using U-NET and DeeplabV3 neural networks, separately with the lowest number of images and the highest accuracy possible. Each of these neural networks carries out image segmentation with different architectures independently and thus we could not combine these two networks for oil spill segmentation. We managed to find two accurate convolutional neural networks (CNNs) for oil spill segmentation because we did not have access to sufficient hardware facilities, like GPU, to train dozens of neural networks. The two networks we used in the present study are among the most well-known and widely used networks. Our purpose was to figure out which network was the best in SAR oil spill detection. Given the limited number of SAR oil spill images and as the input of CNNs needs many images for training, we increased the number of input images to 9801 using the augmentation technique. Then, we carefully identified oil spills with 300 epoch and a batch size of 5 using the Python programming language on the GoogleColab server. The oil spill detection accuracy was 78.8% in the U-NET network and 54% in the DeepLabV3 network. Accordingly, we conclude that the most accurate identification of SAR oil spills in images belong to the U-NET network.

Новітній підхід до застосування нормалізованого диференційного вегетаційного індексу для класифікування аерокосмічних зображень за наявності неповних та неточних даних

Solution of different scientific, ecological and agricultural tasks with the use of aerospace im-ages comprises a procedure of image classification. Classification is one of the most important procedures. Nowadays many supervised and unsupervised classification methods are applied in remote sensing. The most accurate results are obtained through the use of supervised classifica-tion methods. In this paper, there are proposed some new approaches to image classification which are based on supervised classification methods and Normalized Difference Vegetation Index (NDVI). Different values of NDVI are noted to correspond to different classes of objects, such as soil, water, roads, sand, green vegetation, oil spills. Application of Vegetation Index is the first step of classification. Using NDVI, it is possible to select special necessary classes. Af-ter the application of NDVI, such classification methods as the parallelepiped method, Demp-ster’s rule, and Inagaki’s combination rule can be used. The current work describes the main advantages of these classification methods. It has been noted that the use of the parallelepiped method allows easy and quick processing of data. The paper also shows that Dempster’s combi-nation rule and Inagaki’s combination rule can deal with inaccurate and incomplete data from different spectral bands. Moreover, these methods can process conflicting information. Demp-ster-Shafer theory has the advantage of high accuracy and simple calculations. In the paper, there is also considered a numerical example where NDVI and Inagaki’s combination rule has been used for detection and mapping of oil spills. Application of Vegetation Index and such su-pervised classification rules as the parallelepiped method, Dempster’s rule, Inagaki’s combina-tion rule can be applied in ecological monitoring, mapping of petroleum spills, and solving ag-ricultural tasks.

Detection and Analysis of Oil Spill using Image Processing

Detection and Analysis of Oil Spill using Image Processing

Pattern recognition analysis of marine oil spills in airborne passive infrared multispectral remote sensing images.

Pattern recognition methodology was developed for the automated detection of marine oil spills in passive infrared multispectral remote sensing images. The images employed in this work were collected from the Deepwater Horizon oil spill accident in 2010. The imaging instrument for data collection was a downward-looking infrared line scanner equipped with eight optical bandpass filters in the spectral range of 8-12 μm on a fixed-wing aircraft. Oil slicks may show either positive or negative thermal contrast against the surrounding sea water, depending on the sun glint conditions or the oil thickness. Classifiers were developed separately to detect oil with different contrasts by the application of backpropagation neural networks to the preprocessed radiances. Preprocessing strategies included: (1) assembly of training data through k-means clustering analysis; (2) elimination of variation in radiance magnitudes by a customized temperature correction method; (3) removal of sun glint artifacts in images by polynomial correction; and (4) extraction of the most representative features as inputs for the neural networks by a subset selection approach. The classifiers designed to detect oil with positive and negative thermal contrast relative to water achieved overall classification accuracies of 88.7 and 92.2%, respectively. Composite classification images were generated by integrating classification scores produced by the two classifiers. The prediction performance of the classification system was demonstrated through its application to images not involved during the training of the networks.

Comparison between oil spill images and look-alikes: an evaluation of SAR-derived observations of the 2019 oil spill incident along Brazilian waters.

Three SAR-derived observations of dark surface patches along the Northeastern Brazilian coastline by the end of 2019 were misreported in the Brazilian media as oil spill-related. Unfortunately, these observations were misled by false positives or look-alikes. Therefore, this paper aims to technically evaluate these look-alike classes by analyzing image attributes found to be helpful to the identification of ocean targets, including oil spills, rain cells, biofilms, and low wind conditions. We use image augmentation to extend our dataset size and create the probability density function curves. The processing includes image segmentation, optimal attribute extraction, and classification with random forest classifiers. Our results contrast with the open-source oil spill detection system and patch classifier methodology called "RIOSS." Analysis of the feature probability density functions based on optimal attributes is promising since we could capture most of the false positive targets in the three SAR-reported images in 2019. The only exception was the biofilm slick observed on October 28th, where the RIOSS mistakenly classified this organic patch as a low wind region with oil spots. This pitfall is acceptable at this project stage since we had only five biogenic film samples to train the algorithm.

UAV-based remote sensing for the petroleum industry and environmental monitoring: State-of-the-art and perspectives

An unmanned aerial vehicle (UAV), popularly known as a drone, is an aircraft without a human pilot aboard. Recent developments in sensor technology and navigation systems have made drones a powerful and reliable basis for professional data acquisition. Today, the use of UAVs has expanded massively in the civil and commercial sectors and this technology has found its way into almost every industrial sector including the petroleum industry. Drone technology offers a great potential to revolutionize the mapping, monitoring, inspection, and surveillance procedures of the petroleum industry by providing a faster, safer, and more cost-efficient way of mass data collection. This article offers a review of the common UAV platforms and sensor systems and highlights the state-of-the-art and application examples of drone remote sensing in the oil and gas industry. Six broad areas are recognized comprising offshore oil spill detection, oil leakage detection, pipeline monitoring, gas emission sensing, remote facility inspection, petroleum exploration (i.e., land surveying, geologic mapping, and petroleum exploration), and environmental monitoring. Research gaps and open issues along with opportunities for further developments in each of these areas are highlighted. • Common platforms/sensors and application examples are reviewed and highlighted. • Six broad areas are recognized for drone remote sensing in the petroleum industry. • The UAV technology increases the data collection agility, resolution, and efficiency. • Research gaps and opportunities for further developments are highlighted.

Marine Oil-Spill Detection in Multi-Polarization Image-Based SAR on Improved FCN

Marine Oil-Spill Detection in Multi-Polarization Image-Based SAR on Improved FCN

Deep learning-based oil spill detection with LWIR camera

Deep learning-based oil spill detection with LWIR camera

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

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.