Dark Spot Detection from SAR Intensity Imagery with Spatial Density Thresholding for Oil Spill Monitoring

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Sur la convergence de l'estimation conditionnelle itérative

International audienceThe iterative conditional estimation (ICE) is an iterative estimation method of the parameters in the case of incomplete data. Its use asks for relatively weak hypotheses and it can be performed in relatively complex situations, as in triplet Markov models. The aim of this Note is to express a general theorem of convergence of ICE, and to show its applicability in the problem of the estimation of the proportions in a mixture of multivariate distributions.L'estimation conditionnelle itérative (ECI) est une méthode d'estimation itérative des paramètres dans le cas des données incomplètes. Sa mise en oeuvre demande des hypothèses relativement faibles et peut être effectuée dans des situations relativement complexes, comme les champs de Markov cachés à états mixtes. Proposée il y a une quinzaine d'années, l'ECI a été appliquée avec succès aux différents problèmes de segmentation bayésienne non supervisée d'images et des signaux ; cependant, aucun résultat théorique n'est venu étayer ce bon comportement. L'objet de cette note est d'énoncer un théorème général de convergence de l'ECI, et de montrer son applicabilité dans le problème de l'estimation de la proportion dans un mélange de lois multi-variée

Sistema integrado de modelação para apoio à prevenção e mitigação de acidentes de hidrocarbonetos em estuários e orla costeira

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Oceanografia), Universidade de Lisboa, Faculdade de Ciências, 2010A temática dos derrames de hidrocarbonetos continua a ser um assunto de extrema importância dados os graves impactes continuadamente causados no meio marinho. O desenvolvimento de ferramentas para auxilio às autoridades responsáveis pela gestão costeira no combate e mitigação deste tipo de acidentes é prioritário nos planos estratégicos governamentais. Neste trabalho propõe-se um novo sistema integrado de análise de hidrocarbonetos desenvolvido para aplicação a derrames em zonas costeiras, portuárias e oceânicas. Desenvolveuse uma nova metodologia baseada na sinergia da modelação numérica com a detecção remota por satélites. Este sistema integra uma componente de modelação numérica flexível (2D/3D) e um novo algoritmo de segmentação de manchas de hidrocarbonetos observadas em imagens SAR. O algoritmo de detecção remota foi concebido para validar os resultados do sistema de modelos. O sistema de modelação baseia-se numa abordagem Euleriana- Lagrangeana para a resolução dos processos de evolução dos hidrocarbonetos, utilizando malhas não-estruturadas para a representação dos domínios de estudo numa perspectiva multi-escala. O modelo de hidrocarbonetos inclui a maioria dos processos relevantes num derrame à superfície e na coluna de água e inclui um novo algoritmo para a retenção costeira que considera a dinâmica intertidal para aplicação a domínios praias, lagunas e estuários. Realizaram-se diversas aplicações sintéticas e reais que comprovaram a precisão, robustez, fiabilidade e flexibilidade do sistema integrado, com custos computacionais e níveis de complexidade variável. A aplicação do sistema ao caso Prestige permitiu demonstrar que a sinergia entre a modelação numérica e a detecção remota é uma mais-valia para a previsão de derrames de hidrocarbonetos no mar e serviu como base para uma análise qualitativa da influência da precisão da previsão do vento como agente forçador. Futuramente, o sistema poderá ser aplicado na optimização de planos de contingência, sustentando análises de risco, e em sistemas de alerta e aviso para acidentes de poluição.The severe impact of oil spill accidents in the marine environment reinforces the great importance of these environmental catastrophes. The development of tools to assist coastal management authorities in the prevention and mitigation of such accidents remains a priority in governmental strategic plans. This work proposes a new integrated system of analysis developed for application to oil spills in coastal areas, harbours and ocean. A new methodology was developed based on the synergy of numerical modeling with satellite remote sensing. The proposed system integrates a flexible component of numerical modeling (2D/3D) and a new segmentation algorithm for hydrocarbons spills observed in SAR images. The remote sensing algorithm was designed to validate the results of the modeling system. The modeling system is based on an Eulerian-Lagrangian approach for solving the oil spill processes, using unstructured grids for the discretization of the study area in a multi-scale perspective. Most processes occurring at the sea surface and in the water column during an oil spill are included in the integrated model. A new algorithm for costal retention that considers the intertidal dynamics for application in beaches, coastal lagoons and estuaries was also developed. Several synthetic and real applications were performed to verify the accuracy, robustness, reliability and flexibility of the integrated models, with varying computational costs and degrees of complexity. The application of the methodology to the Prestige accident demonstrated that the synergy between remote sensing and numerical modeling is an asset to the prediction of oil spills fate in the marine environment, and was the basis for a qualitative analysis of the wind forcings accuracy e ect. In the future, the new system can be applied in the prevention, prediction and optimization of contingency plans, supporting risk analysis studies, and as a key element in alert and warning systems for coastal pollution accidents.Fundação para a Ciência e a Tecnologia (SFRH/BD/22124/2005); Laboratório Nacional de Engenharia Civil pela cedência de todos os recursos indispensáveis à realização deste trabalho (destacando os projectos "G-Cast: Aplicação da computação GRID num sistema de simulação e previsão da morfodinâmica em zonas costeiras"( GRID/GRI/81733/2006) e "Distribuição e paralelização de modelos numéricos em Hidráulica e Ambiente" pela disponibilização de formação e acesso aos recursos de HPC