Observing Marine Pollution with Synthetic Aperture Radar

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The Costa Concordia last cruise: The first application of high frequency monitoring based on COSMO-SkyMed constellation for wreck removal

AbstractThe Italian vessel Costa Concordia wrecked on January 13th 2012 offshore the Giglio Island (Tuscany, Italy), with the loss of 32 lives. Salvage operation of the vessel started immediately after the wreck. This operation was the largest and most expensive maritime salvage ever attempted on a wrecked ship and it ended in July 2014 when the Costa Concordia was removed from the Giglio Island, and dragged in the port of Genoa where it was dismantled. The refloating and removal phases of the Costa Concordia were monitored, in the period between 14th and 27th of July, exploiting SAR (Synthetic Aperture Radar) images acquired by the X-band COSMO-SkyMed satellite constellation in crisis mode. The main targets of the monitoring system were: (i) the detection of possible spill of pollutant material from the vessel and (ii) to exclude that oil slicks, illegally produced by other vessels, could be improperly linked to the naval convoy during its transit along the route between the Giglio Island and the port of Genoa. Results point out that the adopted monitoring system, through the use of the COSMO-SkyMed constellation, can be profitably employed to monitor emergency phases related to single ship or naval convoy over wide areas and with a suitable temporal coverage. Furthermore, the refloating and removal phases of the Costa Concordia were a success because no pollution was produced during the operations

shoreline detection capability of cosmo skymed and high resolution multispectral images

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Validación y detección automática del transporte dispersivo del emisario submarino de Mar del Plata, Argentina

The submarine outfall of Mar del Plata city at Camet was projected considering the mean and maximum of forecasted sewage discharges, the inner-shelf depth, coliform concentration and its decay (T90) mainly induced by sunlight effect and costal salinity. In 2016 the outfall was operating with a length of 3,810 m and diffusers in the last 526 m. An economical method to monitor its performance in relation to the surroundings, is remote-sensing techniques, applying either visible or radar images. Tidal currents parallel to the coast are responsible for the transport of the sedimentary plume in the far field, after a primary dilution from a depth of 11 m. Visible images (1.5 to 6 m spatial resolution) are effective in monitoring the plume entrained in the upper portion of the water column. These analyses led to study the interaction between waves and coastal currents. Radar images (30 m resolution X and C bands) permit to survey the slick-alike plume that differs from the environment water by the surface roughness. Comparing both techniques visible images can distinguish the different colours of the plume; instead, the radar images are showing the surface roughness from the slick-alike plume. The main advantage of active sensors is that they can map the plume during a cloudy weather and even during night time.El emisario submarino de Mar del Plata en Camet fue proyectado considerando las descargas cloacales promedio y máximas previstas, la profundidad de la plataforma vecina, la concentración de coliformes y el decaimiento (T90) inducido por la luz solar y la salinidad. En 2016 el emisario operaba con una longitud de 3.810 m con difusores en los últimos 526 m. Un método poco oneroso para analizar su comportamiento en relación a su entorno es la aplicación de técnicas de teledetección tanto en el espectro visible como mediante imágenes de radar. Las corrientes de marea paralelas a la costa son responsables de una pluma sedimentaria en el campo lejano, luego de una dilución primaria desde una profundidad de 11 m. Las imágenes visibles (resolución espacial de 1,5 a 6 m) son efectivas para monitorear la pluma extendida en la capa superior del mar. Estos análisis permiten el estudio de la interacción entre olas y corrientes costeras. Las imágenes de radar (resolución de 30 m en las bandas X y C) permiten relevar plumas superficiales semejantes a derrames de aceites por su rugosidad. Comparando ambas técnicas las imágenes visibles pueden distinguir plumas de diferentes colores del agua; por el contrario, las imágenes de radar están mostrando diferencias en la tensión superficial. La principal ventaja de los sensores activos es que permiten monitorear la pluma durante tiempo nuboso incluso sin luz solar

SAR Remote Sensing of Canadian Coastal Waters using Total Variation Optimization Segmentation Approaches

The synthetic aperture radar (SAR) onboard Earth observing satellites has been acknowledged as an integral tool for many applications in monitoring the marine environment. Some of these applications include regional sea-ice monitoring and detection of illegal or accidental oil discharges from ships. Nonetheless, a practicality of the usage of SAR images is greatly hindered by the presence of speckle noises. Such noise must be eliminated or reduced to be utilized in real-world applications to ensure the safety of the marine environment. Thus this thesis presents a novel two-phase total variation optimization segmentation approach to tackle such a challenging task. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise smooth state by minimizing the total variation constraints. In the finite mixture model classification phase, an expectation-maximization method was performed to estimate the final class likelihoods using a Gaussian mixture model. Then a maximum likelihood classification technique was utilized to obtain the final segmented result. For its evaluation, a synthetic image was used to test its effectiveness. Then it was further applied to two distinct real SAR images, X-band COSMO-SkyMed imagery containing verified oil-spills and C-band RADARSAT-2 imagery mainly containing two different sea-ice types to confirm its robustness. Furthermore, other well-established methods were compared with the proposed method to ensure its performance. With the advantage of a short processing time, the visual inspection and quantitative analysis including kappa coefficients and F1 scores of segmentation results confirm the superiority of the proposed method over other existing methods

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

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

Computational Techniques of Oil Spill Detection in Synthetic Aperture Radar Data: Review Cases

In this chapter, a major role of environmental assessment is an oil spill identifies or detected from the coastal region surfaces or marine surroundings. Normally, the oil spills on the coastal regions impact their characteristics of environmental activities. However, these activities are monitoring through several radar satellites and sensor. For those achievable activities detecting or identifying, many researchers developed several approaches. Particularly, this chapter discusses about the detection of oil spill current operational effects on coastal region surfaces. In addition, the current research operations of oil spill characterizations and quality of its impacts, effects of current environmental bio-systems, their control measurement strategies, and its surveillance operations are discussed. Finally, the oil spill detection is done through the SAR image region classification based on its feature extraction. This could be monitored from the image dark region selection through remote sensing techniques