Oil Spill Detection Research Articles

Automatic detection of marine oil spills from polarimetric SAR images using deep Convolutional neural network model

Oil Spills (OS) exert serious threats to marine ecosystems, hence quick and accurate monitoring of OS is of great significance. In recent years, the Synthetic Aperture Radar (SAR) remote sensing technique has been used to monitor OS because of its wide coverage and operating in any weather conditions. However, many studies only resort to manual interpretation when morning OS by SAR images, making the interpretation process low efficiency. In this study, an intelligent method is presented for detecting OS from dual-polarized SAR images using a Deep Convolutional Neural Network (DCNN) based method. Firstly, we designed the OS detection model OSDTAU-Net based on the TransU-Net architecture, which achieves a balance between global perception and local focus by introducing the Transformer and Attention Gate mechanisms into the classical U-Net model. Secondly, we construct the Oil Spill PolSAR dataset from Sentinel-1 satellite images, which further enhances the detection capability of the model through multi-feature fusion of polarimetric information. The experimental results validate that the proposed OS detection model surpasses traditional supervised classification and classic semantic segmentation algorithms, achieving an overall accuracy of 97.09% and F1 score of 89.54%. Furthermore, using the proposed model, we successfully identified two oil spill incidents in the northern Gulf of Mexico. The generated oil spill distribution maps exhibit better continuity and closely align with the actual conditions.

A transfer learning-DCNN based oil spill detection using compact polarimetric SAR data

Oil spills in the marine environment can cause both economic and environmental crises, which underlines the urgent need for effective detection and prevention strategies to mitigate the consequences. Synthetic Aperture Radar (SAR) is one of the commonly used sensors for oil spill detection, and the compact polarimetric (CP) SAR system, with its wide swath width and sufficient polarimetric information, is well suited for this task. This study aims to employ four deep convolutional neural network (DCNN)-based semantic binary segmentation models (i.e., U-Net, LinkNet, FPN, and PSPNet) to detect oil spills using simulated compact polarimetry in hybrid-pol mode. In the deployed methodology, we used transfer learning to improve the adaptability of the models to different sensors. We efficiently adapted the built oil spill detection models on UAVSAR to data of RADARSAT-2 while retaining the essential features and knowledge from pre-trained models by fine-tuning them. The results showed the potential of these models in oil spill detection. The PSPNet model, as the most accurate, achieved an overall accuracy (OA) of 96.00% and a kappa coefficient of 91.30% on the UAVSAR image. After fine-tuning, it yielded an OA of 98.68% and a kappa coefficient of 92.71% on the RADARSAT-2 image.

Ship detection using ensemble deep learning techniques from synthetic aperture radar imagery

Synthetic Aperture Radar (SAR) integrated with deep learning has been widely used in several military and civilian applications, such as border patrolling, to monitor and regulate the movement of people and goods across land, air, and maritime borders. Amongst these, maritime borders confront different threats and challenges. Therefore, SAR-based ship detection becomes essential for naval surveillance in marine traffic management, oil spill detection, illegal fishing, and maritime piracy. However, the model becomes insensitive to small ships due to the wide-scale variance and uneven distribution of ship sizes in SAR images. This increases the difficulties associated with ship recognition, which triggers several false alarms. To effectively address these difficulties, the present work proposes an ensemble model (eYOLO) based on YOLOv4 and YOLOv5. The model utilizes a weighted box fusion technique to fuse the outputs of YOLOv4 and YOLOv5. Also, a generalized intersection over union loss has been adopted in eYOLO which ensures the increased generalization capability of the model with reduced scale sensitivity. The model has been developed end-to-end, and its performance has been validated against other reported results using an open-source SAR-ship dataset. The obtained results authorize the effectiveness of eYOLO in multi-scale ship detection with an F1score and mAP of 91.49% and 92.00%, respectively. This highlights the efficacy of eYOLO in multi-scale ship detection using SAR imagery.

Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring.

THE NEXUS BETWEEN CORRUPTION AND RESOURCE THEFT: IMPLICATIONS FOR NATIONAL SECURITY IN RESOURCE-RICH NATIONS

The nexus between crude oil theft, corruption, and national security in Nigeria is complex and multifaceted. Oil theft directly impacts the nation's economy by reducing government revenue, which in turn limits the resources available for critical public services, including security and law enforcement. Corruption, by weakening governance and fostering impunity, compounds these security threats, allowing criminal networks to thrive This study investigates the intricate relationship between crude oil theft, corruption, and national security in Nigeria. The study utilised a longitudinal research design, examining secondary data from 2013 to 2023, including metrics such as oil theft, political corruption, violent events, and fatalities. Data from sources like the Armed Conflict Location & Event Data Project (ACLED), the National Oil Spill Detection and Response Agency (NOSDRA), and the World Bank's political corruption index were analysed using Poisson regression to assess the effects of oil theft and corruption on national security. Findings revealed that crude oil theft has a statistically significant positive relationship with violent events, while political corruption, represented by the political corruption index, shows a strong negative relationship with security, with an estimate of - 26.475. This could imply that corruption may sometimes act as a stabilising force whereby, in corrupt systems, those in power might suppress violence to maintain order and avoid public attention that could threaten their positions. However, political corruption has a substantial positive effect on fatalities, with an estimate of 19.589. This highlights that as corruption increases, fatalities rise significantly, pointing to the detrimental impact of corruption on state institutions and the rule of law. These findings also suggest that while crude oil theft directly contributes to insecurity, political corruption may reduce overt violence by maintaining fragile stability. Therefore, addressing these issues requires a nuanced approach. Policymakers must recognise that combating corruption and oil theft alone will not be sufficient to ensure national security; instead, strategies should tackle both issues simultaneously, understanding that reducing visible violence does not always eliminate the underlying causes of insecurity.

A dataset of drone-captured, segmented images for oil spill detection in port environments

The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhance polluter accountability, potentially deterring future incidents. Currently, there’s a scarcity of datasets employing RGB images for oil spill detection in maritime settings. This paper presents a unique, annotated dataset aimed at addressing this gap, leveraging a neural network for analysis on both desktop and edge computing platforms. The dataset, captured via drone, comprises 1268 images categorized into oil, water, and other, with a convolutional neural network trained using an Unet model architecture achieving an F1 score of 0.71 for oil detection. This underscores the dataset’s practicality for real-world applications, offering crucial resources for environmental conservation in port environments.

Marine Oil Pollution Monitoring Based on a Morphological Attention U-Net Using SAR Images.

This study proposed an improved full-scale aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas using synthetic aperture radar (SAR) images. The convolutional block attention module (CBAM) in the FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model's overconfidence in majority class samples while improving the model's generalization ability. The detection performance of the improved FA-MobileUNet model reached an mIoU (mean intersection over union) of 84.55%, which was 17.15% higher than that of the original U-Net model. The effectiveness of the proposed model was then verified using the oil pollution incidents that significantly impacted Taiwan's marine environment. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

An affordable operational oil spill monitoring system in action: A diachronic multiplatform analysis of recent incidents in the southern Gulf of Mexico

The coexistence of marine sensitive areas with the oil industry requires robust preparedness and rapid response capabilities for monitoring and mitigating oil spill events. Scientifically proven satellite-based methods for the visual detection of oil spills are widely recognized as effective, low-cost, transferable, scalable, and operational solutions, particularly in developing economies. Following meticulous design and implementation, we adopted and executed a relatively low-cost operational monitoring and alert system for oil spill detection over the ocean surface and alert issuance. We analyzed over 1500 satellite images, issuing over 70 warning reports on oil slicks and spills in the southern Gulf of Mexico. To assess the system’s efficiency and performance, we leveraged data from three major oil spill incidents in the study region during June and July of 2023 in the study region, covering a maximum area of 669 km2 and tracked for 12 to 24 days. We documented the evolution of these oil spills by integrating satellite sensing data with on-site Lagrangian drifting buoys, a network of high-frequency radars, and citizen reports to validate the outcomes of this system. We generated timely technical information on the spill’s evolution, informing decision-makers and local community leaders to strengthen their mitigation response capabilities. Additionally, we developed a robust database with spectral and spatiotemporal features of satellite-detected oil, thereby contributing to advancing the scientific understanding of sea surface dynamics related to natural and anthropogenic oil sources. This study also highlights immediate-, medium-, and long-term research agendas and establishes a reference for a sustained, transferable, and operational oil spill monitoring system.

Implementation of a semi-automatic approach for detection and extraction of oil slicks using Sentinel-1: Case study of the Moroccan exclusive economic zone

Due to its strategic geographic location, Morocco experiences heavy ship traffic, which inevitably has consequences. Ship accidents and illegal discharges, commonly referred to as oil spills, are frequent and pose significant harm to the marine ecosystem. Therefore, employing new technologies, such as satellite monitoring, is crucial and will greatly enhance the ability to monitor and assess these oil spills. The present work is based on Sentinel-1 SAR images and aims to take into evidence the extent of the hydrocarbon marine pollution in all of the Moroccan EEZ. More than 220 possible cases were detected and mapped during the last decade (2014–2024). A batch process was developed to automate the detection of oil spills by identifying areas with reduced Normalized Radar Cross Section (NRCS) values. This process incorporates corrections, masks, wind field estimation, and oil clustering. The method successfully detected approximately 80% of the cases between Kenitra and Casablanca. The closest detected spill is located 1 km from the coast, the largest spill covers an area of 90 km2, and the most extended spill measures about 145 km. This study can help raise awareness among governmental authorities about potential environmental damage and can be used to train artificial intelligence models, which could simulate oil slick propagation.

Improvement of K-Means clustering algorithm using genetic algorithm for spatial analysis of the oil spill detection in polarimetric SAR images

Improvement of K-Means clustering algorithm using genetic algorithm for spatial analysis of the oil spill detection in polarimetric SAR images

Detection of Oil Spill in SAR Image Using an Improved DeepLabV3.

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model.

Developing a Comprehensive Oil Spill Detection Model for Marine Environments

Developing a Comprehensive Oil Spill Detection Model for Marine Environments

Utilizing deep learning algorithms for automated oil spill detection in medium resolution optical imagery

This study evaluates the performance of three typical convolutional neural network based deep learning algorithms for oil spill detection using medium-resolution optical satellite imagery from Sentinel-2 MSI, Landsat-8 OLI, and Landsat-9 OLI2. Oil slick training and validation dataset were created through a semi-automatic labeling approach, based on chronic and accidental oil spill cases reported worldwide. The research enhances UNet, BiSeNetV2, and DeepLabV3+ architectures by integrating attention mechanisms including the Squeeze-and-Excitation module (SE), Convolutional Block Attention Module (CBAM), and a Simple, parameter-free Attention Module (SimAM), analyzing the optimal model for oil spill detection. Notably, UNet integrated with CBAM, especially with sun glint as a feature, significantly outperformed others, achieving a micro-average F1 score of 88.8 %. This research highlights deep learning's potential in optical remote sensing for oil spill detection, stressing its escalating relevance with the growing deployment of medium- to high-resolution optical satellites.

Experiment for Oil Spill Detection Based on Dual-Frequency QZSS Reflected Signals Using Drone-Borne GNSS-R

Experiment for Oil Spill Detection Based on Dual-Frequency QZSS Reflected Signals Using Drone-Borne GNSS-R

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts.

The Role of Remote Sensing Data in Assessing Human Impact on the Environment

The article focuses on the application of remote sensing methods to assess the impact of human activities on the environment. Emphasis is placed on the need to accurately monitor anthropogenic impacts, such as deforestation and water pollution, to maintain environmental balance. The Materials and Methods section describes passive and active sensing methods, including infrared imaging, radar sensing, and laser scanning. Passive sensing analyzes the spectral characteristics of objects, which is useful for monitoring vegetation changes, while radar sensing can detect oil spills even in adverse weather conditions. Laser sensing is used for precise measurement of terrain changes. The Results and Discussion section provides examples of practical application of these methods: analysis of deforestation in the Kirov region using Landsat 8 and 9 satellite data, and investigation of an oil spill in Novorossiysk using radar images from the Sentinel-1 satellite. These examples demonstrate the effectiveness of remote sensing for monitoring changes and identifying the negative consequences of human activities. The conclusion highlights the importance of remote sensing as a powerful tool for environmental research and monitoring.

Advancements in remote sensing technologies for oil spill detection: Policy and implementation

Advancements in remote sensing technologies for oil spill detection: Policy and implementation

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA.

Marine oil spill detection and segmentation in SAR data with two steps Deep Learning framework

Marine oil spills pose significant ecological and economic threats worldwide, requiring effective decision-making tools. In this study, the optimal parameters, and configurations for Deep Learning models in oil spill classification and segmentation using Sentinel-1 SAR imagery were identified. First, a new Sentinel-1 image dataset was created. Ninety CNN configurations were explored for classification by varying the number of convolutional layers, filters, hidden layers, and neurons in each layer. For segmentation tasks, MLP and U-Net models were evaluated with variations in convolutional layers, filters, and incorporation of IoU and Focal Loss. The results indicated that a CNN model with six layers, 32 filters, and two hidden layers achieved 99 % classification accuracy. For segmentation, the U-Net model with more layers and filters using Focal Loss achieved 99 % accuracy and 96 % IoU. Therefore, a CNN and U-Net framework was proposed that achieves an overall accuracy of 95 % and an IoU of 90 %.

Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection.

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network's competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.