Fine-Scale Features on the Sea Surface in SAR Satellite Imagery - Part 2: Numerical Modeling

With the advent of the new generation of synthetic aperture radar (SAR) satellites, it has become possible to resolve fine-scale features on the sea surface on the scale of meters. The proper identification of sea surface signatures in SAR imagery can be challenging, since some features may be due to atmospheric distortions (gravity waves, squall lines) or anthropogenic influences (slicks), and may not be related to dynamic processes in the upper ocean. In order to improve our understanding of the nature of fine-scale features on the sea surface and their signature in SAR, we have conducted high-resolution numerical simulations combining a three-dimensional non-hydrostatic computational fluid dynamics model with a radar imaging model. The surface velocity field from the hydrodynamic model is used as input to the radar imaging model. The combined approach reproduces the sea surface signatures in SAR of ship wakes, low-density plumes, and internal waves in a stratified environment. The numerical results are consistent with observations reported in a companion paper on in situ measurements during SAR satellite overpasses. Ocean surface and internal waves are also known to produce a measurable signal in the ocean magnetic field. This paper explores the use of computational fluid dynamics to investigate the magnetic signatures of oceanic processes. This potentially provides a link between SAR signatures of transient ocean dynamics and magnetic field fluctuations in the ocean. We suggest that combining SAR imagery with data from ocean magnetometers may be useful as an additional maritime sensing method. The new approach presented in this work can be extended to other dynamic processes in the upper ocean, including fronts and eddies, and can be a valuable tool for the interpretation of SAR images of the ocean surface

Selected Papers from the 2018 IEEE International Workshop on Metrology for the Sea

This Special Issue is devoted to recent developments in instrumentation and measurement techniques applied to the marine field. ¶The sea is the medium that has allowed people to travel from one continent to another using vessels, even today despite the use of aircraft. It has also been acting as a great reservoir and source of food for all living beings. However, for many generations, it served as a landfill for depositing conventional and nuclear wastes, especially in its deep seabeds, and we are assisting in a race to exploit minerals and resources, different from foods, encompassed in it. Its health is a great challenge for the survival of all humanity since it is one of the most important environmental components targeted by global warming. ¶ As everyone may know, measuring is a step that generates substantial knowledge about a phenomenon or an asset, which is the basis for proposing correct solutions and making proper decisions. However, measurements in the sea environment pose unique difficulties and opportunities, which is made clear from the research results presented in this Special Issue

Efficient SAR MTI simulator of marine scenes

Tècniques de detecció de moviment amb radars d'apertura sintètica multicanals sobre escenaris marítims.[ANGLÈS] Multichannel spaceborne and airborne synthetic aperture radars (SAR) offer the opportunity to monitor maritime traffic through specially designed instruments and applying a suitable signal processing in order to reject sea surface clutter. These processing techniques are known as Moving Target Indication techniques (MTI) and the choice of the most adequate method depends on the radar system and operating environment. In maritime scenes the seas presents a complicated clutter whose temporal/spatial coherence models and background reflectivity depends on a large number of factors and are still subject of research. Moreover the targets kinematics are influenced by the sea conditions, producing in some situations high alterations in the imaged target. These aspects make difficult the detectability analysis of vessels in maritime scenarios, requiring both theoretical models and numerical simulations. This thesis looks into the few available MTI techniques and deals experimentally with them in a developed simulator for maritime SAR images. The results are also presented in a image format, giving the sequence for one trial simulation and the asymptotic probability of detection for the simulated conditions.[CASTELLÀ] Los radares de apertura sintética (SAR) multicanal a bordo de satélites o plataformas aerotransportadas ofrecen la oportunidad de monitorizar el tráfico marítimo a través de instrumentos especialmente diseñados y procesando los datos recibidos de forma adecuada para rechazar la señal provocada por la reflexión del mar. A estas técnicas se las conoce como Moving Target Indication techniques (MTI) y la elección de la más adecuada depende del sistema y del entorno de aplicación. En escenarios marinos, el mar presenta un clutter complicado de modelar, cuya coherencia espacio-temporal y reflectividad radar dependen de un gran número de factores que hoy en día todavía siguen siendo investigados. Por otra parte los parámetros dinámicos del target estan influenciados por las condiciones del mar, produciendo en algunas situaciones graves alteraciones en la formación de la imagen. Estos aspectos dificultan el análisis de la detección de las embarcaciones, requiriendo modelos teóricos y simulaciones numéricas. Este Proyecto Final de Carrera investiga las técnicas MTI disponibles, aplicándolas sobre las imágenes marítimas generadas por un simulador SAR. Los resultados son la generación de los productos MTI en formato imagen y el cálculo de la probabilidad de detección para cada target.[CATALÀ] Els radars d'obertura sintètica (SAR) multicanal embarcats en satèl·lits o plataformes aerotransportades ofereixen l'oportunitat de monitoritzar el tràfic marítim a través d'instruments especialment dissenyats i processant les dades rebudes de forma adequada per rebutjar la senyal provocada per la reflexió del mar. A aquestes tècniques se les coneix com Moving Target indication techniques (MTI) i l'elecció de la més adequada depèn del sistema i de l'entorn d'aplicació. En escenaris marins, el mar presenta un clutter complicat de modelar, la coherència espai-temporal i reflectivitat radar depenen d'un gran nombre de factors que avui dia encara segueixen sent investigats. D'altra banda els paràmetres dinàmics del target estan influenciats per les condicions de la mar, produint en algunes situacions greus alteracions en la formació de la imatge. Aquests aspectes dificulten l'anàlisi de la detecció de les embarcacions, requerint models teòrics i simulacions numèriques. Aquest Projecte Final de Carrera investiga les tècniques MTI disponibles, aplicant-les sobre les imatges marítimes generades per un simulador SAR. Els resultats són la generació dels productes MTI en format imatge i el càlcul de la probabilitat asimptòtica de detecció per a cada target

Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms

Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder–decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation

First Analyses of Sentinel-1 Images for Maritime Surveillance

Sentinel-1 is the European Synthetic Aperture Radar (SAR) satellite operational since 3 October 2014. The SAR’s characteristics should make it suitable for maritime surveillance (ship detection), and it will routinely collect a large amount of maritime imagery over European and global seas. After its launch in April 2014, preliminary data have been made available to limited users in the satellite’s commissioning phase, and since the start of the operational phase data are available to the general public. These early data have been used to assess the quality of Sentinel-1 images and their suitability for ship detection. This was partly done by using the JRC’s ship detection software SUMO, after adaptation to ingest and process Sentinel-1 data. It is found that the sensor lives up to its specifications, thereby making it very useful for maritime surveillance thanks to its combination of wide swath and low noise at the medium resolution with which it will mostly be operated (“IW” and “EW” modes).JRC.G.3-Maritime affair

OIL SPILL MODELING FOR IMPROVED RESPONSE TO ARCTIC MARITIME SPILLS: THE PATH FORWARD

Maritime shipping and natural resource development in the Arctic are projected to increase as sea ice coverage decreases, resulting in a greater probability of more and larger oil spills. The increasing risk of Arctic spills emphasizes the need to identify the state-of-the-art oil trajectory and sea ice models and the potential for their integration. The Oil Spill Modeling for Improved Response to Arctic Maritime Spills: The Path Forward (AMSM) project, funded by the Arctic Domain Awareness Center (ADAC), provides a structured approach to gather expert advice to address U.S. Coast Guard (USCG) Federal On-Scene Coordinator (FOSC) core needs for decision-making. The National Oceanic & Atmospheric Administration (NOAA) Office of Response & Restoration (OR&R) provides scientific support to the USCG FOSC during oil spill response. As part of this scientific support, NOAA OR&R supplies decision support models that predict the fate (including chemical and physical weathering) and transport of spilled oil. Oil spill modeling in the Arctic faces many unique challenges including limited availability of environmental data (e.g., currents, wind, ice characteristics) at fine spatial and temporal resolution to feed models. Despite these challenges, OR&R’s modeling products must provide adequate spill trajectory predictions, so that response efforts minimize economic, cultural and environmental impacts, including those to species, habitats and food supplies. The AMSM project addressed the unique needs and challenges associated with Arctic spill response by: (1) identifying state-of-the-art oil spill and sea ice models, (2) recommending new components and algorithms for oil and ice interactions, (3) proposing methods for improving communication of model output uncertainty, and (4) developing methods for coordinating oil and ice modeling efforts

MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

Abstract. In this paper we use MEDSLIK-II, a Lagrangian marine surface oil spill model described in Part 1 (De Dominicis et al., 2013), to simulate oil slick transport and transformation processes for realistic oceanic cases, where satellite or drifting buoys data are available for verification. The model is coupled with operational oceanographic currents, atmospheric analyses winds and remote sensing data for initialization. The sensitivity of the oil spill simulations to several model parameterizations is analyzed and the results are validated using surface drifters, SAR (synthetic aperture radar) and optical satellite images in different regions of the Mediterranean Sea. It is found that the forecast skill of Lagrangian trajectories largely depends on the accuracy of the Eulerian ocean currents: the operational models give useful estimates of currents, but high-frequency (hourly) and high-spatial resolution is required, and the Stokes drift velocity has to be added, especially in coastal areas. From a numerical point of view, it is found that a realistic oil concentration reconstruction is obtained using an oil tracer grid resolution of about 100 m, with at least 100 000 Lagrangian particles. Moreover, sensitivity experiments to uncertain model parameters show that the knowledge of oil type and slick thickness are, among all the others, key model parameters affecting the simulation results. Considering acceptable for the simulated trajectories a maximum spatial error of the order of three times the horizontal resolution of the Eulerian ocean currents, the predictability skill for particle trajectories is from 1 to 2.5 days depending on the specific current regime. This suggests that re-initialization of the simulations is required every day