scholarly journals Leveraging Satellite Sensors for Oil Spill Detection

Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Sarah Derouin
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Oil Spill Detection ◽  
Remote Sensors ◽  
Satellite Sensors

By using multiple remote sensors, scientists can quickly estimate the nature and thickness of oil spills—important factors for containment efforts.

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 OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 4 (11) ◽  
pp. 1-10
Author(s):  
M. Sornam
Keyword(s):  
Image Segmentation ◽  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Oil Spill Detection ◽  
Otsu Method ◽  
Artificial Neural ◽  
Propagation Network

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.

A Model Based Approach to Radar Oil Spill Detection

2014 ◽  
Vol 2014 (1) ◽  
pp. 2228-2241
Author(s):  
Torstein Pedersen ◽  
Javier Perez ◽  
Jos Van Heseen
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Radar Image ◽  
Monitoring Systems ◽  
Multiple Sources ◽  
Threshold Detector ◽  
Ir Camera ◽  
Oil Spill Detection ◽  
Model Based ◽  
Primary Driver

ABSTRACT A typical oil spill recovery vessel has been historically outfitted with an oil spill detection (OSD) radar. During an oil spill recovery operation, there is a dedicated operator who is responsible for interpreting information from the radar image. Industry developments over the last several years now require that an OSD radar automatically detect and track an oil spill. There are two primary needs driving this development. The first is that OSD systems and operations are becoming more sophisticated; automatic OSD aids for a more efficient oil spill operation where an operator's attention may be directed to a potential spill. The automatic OSD also aids a multi-sensor system; one such example is where an OSD radar is used to steer an IR camera to a candidate spill for more detailed evaluation or validation. The other primary driver for automatic OSD is for monitoring systems, which serve for early warning. Monitoring systems may be found along coastal installations or oil platforms. The automatic spill detection functionality of an OSD system may be implemented in different levels of sophistication. Perhaps the simplest configuration is one that uses fixed thresholds relative to the image for alarming whether a region in a radar image is a spill or not. The benefit of simple threshold detector is that it is easy to implement in software. The weakness is that it is prone to both lower overall detection rate and high false alarm rate. A more robust automatic spill detection method is one that treats it as an image-processing problem. The paper here presents a model based OSD. Generation of confidence maps is central to the method and provides an indication of the likelihood of oil. Inputs to the confidence maps come from multiple sources, several of which are based on uniquely constructed models. Among these is a histogram comparator, which scans a radar image and compares the data to reference models from real oil spills. A discussion of the methods used focuses on (a) the necessary steps prior to the confidence map construction, (b) how the confidence maps are layered with inputs, (c) how the information in the confidence maps is transitioned into the detection of oil, (d) and finally alarming.

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 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.

NEW SPACE-BORNE SENSORS FOR OIL SPILL RESPONSE

2001 ◽  
Vol 2001 (2) ◽  
pp. 911-916 ◽  
Author(s):  
Carl E. Brown ◽  
Mervin F. Fingas
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Optical Sensors ◽  
Oil Spills ◽  
Weather Forecasting ◽  
Radar Data ◽  
Operating Characteristics ◽  
Strategic Role ◽  
Satellite Sensors ◽  
Oil Spill Response

ABSTRACT In the next few years, several new satellite sensors will be launched by various national remote-sensing/earth observation agencies around the globe. It is hoped that these space-borne sensors will provide oil spill response personnel with more than just a synoptic overview of the spill scene. The state-of-the-art capabilities of these new sensors should provide responders with information that can be used in a tactical role as opposed to older-generation sensors that perform a strictly strategic role. Of primary use to spill response coordinators is the Synthetic Aperture Radar (SAR) sensor. The next generation of SAR satellites will have enhanced capabilities when compared to their predecessors. The enhancements include the addition of Polarimetric modes for satellites, including Envisat-1 and RADARSAT-2. RADARSAT-2 will be quad-polarimetric, with resolutions of 8 × 8 m in Polarimetric mode and down to 3 × 3 m in co- or cross-pole modes. The ASAR sensor on Envisat-1 will follow up the successful missions of the European Space Agencies ERS-1, −2 satellites. ASAR will have an alternating polarization mode, and transmit and receive polarization can be selected, thus allowing scenes to be imaged simultaneously in two polarizations. In addition to SAR satellites, several new optical satellites have been or will be launched over the next few years. While optical sensors often are plagued by periods of foul weather that frequently accompany oil spills, some of these sensors will provide valuable information that can be used in conjunction with the radar data in a corroborative fashion. The most useful of the new optical satellites might well be those used to collect data for weather forecasting. This paper will review the operating characteristics and modes of recent and planned satellite sensors, with an eye toward their usefulness for tactical remote sensing of oil spills.

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.

scholarly journals Reflection-coefficient experimental extraction from S21- parameter for radar oil-spill detection application

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Bilal Hammoud 1,2 ◽  
Fabien Ndagijimana 2 ◽  
Ghaleb Faour 3 ◽  
Hussam Ayad 1 ◽  
Majida Fadlallah 1 ◽  
...  
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Sea Water ◽  
Detection System ◽  
Theoretical Calculations ◽  
Monitoring Systems ◽  
Reflection Coefficients ◽  
Oil Spill Detection ◽  
Long Period ◽  
Maritime Environment

Oil spill in sea water is one of the main accidents that affect significantly the maritime environment over a long period of time. Knowing the severe influence of oil spills on the ecosystem, it is crucial to have oil spill detecting and monitoring systems for quick intervention and danger containment. In our project, we propose the usage of drones as an oil spill detection system. The drones will be implementing different previously developed multi-frequency approaches for the detection. The effectiveness of such techniques is based on the accuracy of the data collected and their match to the theory. This journal presents a method for the remote extraction of reflection coefficients from multilayer structure modeling an oil spill in sea water. The experimental results for the reflectivity extraction validate the theoretical calculations and allow the implementation of different algorithms based on the statistical information taken directly from the site.

scholarly journals Oil Spill Detection Using Machine Learning and Infrared Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4090
Author(s):  
Thomas De Kerf ◽  
Jona Gladines ◽  
Seppe Sels ◽  
Steve Vanlanduit
Keyword(s):  
Neural Network ◽  
Oil Spill ◽  
Infrared Imaging ◽  
Oil Spills ◽  
Infrared Images ◽  
Ir Camera ◽  
Oil Spill Detection ◽  
Imaging Camera ◽  
Rgb Images ◽  
Trained Network

The detection of oil spills in water is a frequently researched area, but most of the research has been based on very large patches of crude oil on offshore areas. We present a novel framework for detecting oil spills inside a port environment, while using unmanned areal vehicles (UAV) and a thermal infrared (IR) camera. This framework is split into a training part and an operational part. In the training part, we present a process for automatically annotating RGB images and matching them with the IR images in order to create a dataset. The infrared imaging camera is crucial to be able to detect oil spills during nighttime. This dataset is then used to train on a convolutional neural network (CNN). Seven different CNN segmentation architectures and eight different feature extractors are tested in order to find the best suited combination for this task. In the operational part, we propose a method to have a real-time, onboard UAV oil spill detection using the pre-trained network and a low power interference device. A controlled experiment in the port of Antwerp showed that we are able to achieve an accuracy of 89% while only using the IR camera.

scholarly journals Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training

2021 ◽  
Vol 13 (12) ◽  
pp. 2378
Author(s):  
Yongqing Li ◽  
Xinrong Lyu ◽  
Alejandro C. Frery ◽  
Peng Ren
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Multiple Scales ◽  
Training Data ◽  
Small Data ◽  
Observation Data ◽  
Training Procedure ◽  
Oil Spill Detection ◽  
Adversarial Network ◽  
Adversarial Networks

We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model’s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.

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