The InflateSAR Campaign: Evaluating SAR Identification Capabilities of Distressed Refugee Boats

Most of the recent research in the field of marine target detection has been concentrating on ships with large metallic parts. The focus of this work is on much more challenging targets represented by small rubber inflatables. They are of importance, since in recent years they have largely been used by migrants to cross the Mediterranean Sea between Libya and Europe. The motivation of this research is to mitigate the ongoing humanitarian crisis at Europe’s southern borders. These boats, packed with up to 200 people, are in no way suitable to cross the Mediterranean Sea or any other big water body and are in distress from the moment of departure. The establishment of a satellite-based surveillance infrastructure could considerably support search and rescue missions in the Mediterranean Sea, reduce the number of such boats being missed and mitigate the ongoing death in the open ocean. In this work we describe and analyze data from the InflateSAR acquisition campaign, wherein we gathered multiple-platform SAR imagery of an original refugee inflatable. The test site for this campaign is a lake which provides background clutter that is more predictable. The analysis considered a sum of experiments, enabling investigations of a broad range of scene settings, such as the vessel’s orientation, superstructures and speed. We assess their impact on the detectability of the chosen target under different sensor parameters, such as polarimetry, resolution and incidence angle. Results show that TerraSAR-X Spotlight and Stripmap modes offer good capabilities to potentially detect those types of boats in distress. Low incidence angles and cross-polarization decrease the chance of a successful identification, whereas a fully occupied inflatable, orthogonally oriented to the line of sight, seems to be better visible than an empty one. The polarimetric analyses prove the vessel’s different polarimetric behavior in comparison with the water surface, especially when it comes to entropy. The analysis considered state-of-the-art methodologies with single polarization and dual polarization channels. Finally, different metrics are used to discuss whether and to which extent the results are applicable to other open ocean datasets. This paper does not introduce any vessel detection or classification algorithm from SAR images. Rather, its results aim at paving the way to the design and the development of a specially tailored detection algorithm for small rubber inflatables

Maritime Moving Target Detection, Tracking and Geocoding Using Range-Compressed Airborne Radar Data

Eine regelmäßige und großflächige überwachung des Schiffsverkehrs gewinnt zunehmend an Bedeutung, vor allem auch um maritime Gefahrenlagen und illegale Aktivitäten rechtzeitig zu erkennen. Heutzutage werden dafür überwiegend das automatische Identifikationssystem (AIS) und stationäre Radarstationen an den Küsten eingesetzt. Luft- und weltraumgestützte Radarsensoren, die unabhängig vom Wetter und Tageslicht Daten liefern, können die vorgenannten Systeme sehr gut ergänzen. So können sie beispielsweise Schiffe detektieren, die nicht mit AIS-Transpondern ausgestattet sind oder die sich außerhalb der Reichweite der stationären AIS- und Radarstationen befinden. Luftgestützte Radarsensoren ermöglichen eine quasi-kontinuierliche Beobachtung von räumlich begrenzten Gebieten. Im Gegensatz dazu bieten weltraumgestützte Radare eine große räumliche Abdeckung, haben aber den Nachteil einer geringeren temporalen Abdeckung. In dieser Dissertation wird ein umfassendes Konzept für die Verarbeitung von Radardaten für die Schiffsverkehr-überwachung mit luftgestützten Radarsensoren vorgestellt. Die Hauptkomponenten dieses Konzepts sind die Detektion, das Tracking, die Geokodierung, die Bildgebung und die Fusion mit AIS-Daten. Im Rahmen der Dissertation wurden neuartige Algorithmen für die ersten drei Komponenten entwickelt. Die Algorithmen sind so aufgebaut, dass sie sich prinzipiell für zukünftige Echtzeitanwendungen eignen, die eine Verarbeitung an Bord der Radarplattform erfordern. Darüber hinaus eignen sich die Algorithmen auch für beliebige, nicht-lineare Flugpfade der Radarplattform. Sie sind auch robust gegenüber Lagewinkeländerungen, die während der Datenerfassung aufgrund von Luftturbulenzen jederzeit auftreten können. Die für die Untersuchungen verwendeten Daten sind ausschließlich entfernungskomprimierte Radardaten. Da das Signal-Rausch-Verhältnis von Flugzeugradar-Daten im Allgemeinen sehr hoch ist, benötigen die neuentwickelten Algorithmen keine vollständig fokussierten Radarbilder. Dies reduziert die Gesamtverarbeitungszeit erheblich und ebnet den Weg für zukünftige Echtzeitanwendungen. Der entwickelte neuartige Schiffsdetektor arbeitet direkt im Entfernungs-Doppler-Bereich mit sehr kurzen kohärenten Verarbeitungsintervallen (CPIs) der entfernungskomprimierten Radardaten. Aufgrund der sehr kurzen CPIs werden die detektierten Ziele im Dopplerbereich fokussiert abgebildet. Wenn sich die Schiffe zusätzlich mit einer bestimmten Radialgeschwindigkeit bewegen, werden ihre Signale aus dem Clutter-Bereich hinausgeschoben. Dies erhöht das Verhältnis von Signal- zu Clutter-Energie und verbessert somit die Detektierbarkeit. Die Genauigkeit der Detektion hängt stark von der Qualität der von der Meeresoberfläche rückgestreuten Radardaten ab, die für die Schätzung der Clutter-Statistik verwendet werden. Diese wird benötigt, um einen Detektions-Schwellenwert für eine konstante Fehlalarmrate (CFAR) abzuleiten und die Anzahl der Fehlalarme niedrig zu halten. Daher umfasst der vorgeschlagene Detektor auch eine neuartige Methode zur automatischen Extraktion von Trainingsdaten für die Statistikschätzung sowie geeignete Ozean-Clutter-Modelle. Da es sich bei Schiffen um ausgedehnte Ziele handelt, die in hochauflösenden Radardaten mehr als eine Auflösungszelle belegen, werden nach der Detektion mehrere von einem Ziel stammende Pixel zu einem physischen Objekten zusammengefasst, das dann in aufeinanderfolgenden CPIs mit Hilfe eines Bewegungsmodells und eines neuen Mehrzielverfolgungs-Algorithmus (Multi-Target Tracking) getrackt wird. Während des Trackings werden falsche Zielspuren und Geisterzielspuren automatisch erkannt und durch ein leistungsfähiges datenbankbasiertes Track-Management-System terminiert. Die Zielspuren im Entfernungs-Doppler-Bereich werden geokodiert bzw. auf den Boden projiziert, nachdem die Einfallswinkel (DOA) aller Track-Punkte geschätzt wurden. Es werden verschiedene Methoden zur Schätzung der DOA-Winkel für ausgedehnte Ziele vorgeschlagen und anhand von echten Radardaten, die Signale von echten Schiffen beinhalten, bewertet

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Data Processing and Modeling on Volcanic and Seismic Areas

This special volume aims to collecg new ideas and contributions at the frontier between the fields of data handling, processing and modeling for volcanic and seismic systems. Technological evolution, as well as the increasing availability of new sensors and platforms, and freely available data, pose a new challenge to the scientific community in the development new tools and methods that can integrate and process different information. The recent growth in multi-sensor monitoring networks and satellites, along with the exponential increase in the spatiotemporal data, has revealed an increasingly compelling need to develop data processing, analysis and modeling tools. Data processing, analysis and modeling techniques may allow significant information to be identified and integrated into volcanic/seismological monitoring systems. The newly developed technology is expected to improve operational hazard detection, alerting, and management abilities

Offshore oil seepage visible from space : a Synthetic Aperture Radar (SAR) based automatic detection, mapping and quantification system

Offshore oil seepage is believed to be the largest source of marine oil, yet very few of their locations and seepage fluxes have been discovered and reported. Natural oil seep sites are important as they serve as potential energy sources and because they are hosts to a very varied marine ecosystem. These seeps can also be associated with gas hydrates and methane emissions and hence, locating natural oil seeps can provide locations where the sources of greenhouse gases could be studied and quantified. A quantification of the amount of crude oil released from natural oil seeps is important as it can be used to set a background against which the excess anthropogenic sources of marine oil can be checked. This will provide an estimate of the 'contamination' of marine waters from anthropogenic sources. Until the onset of remote sensing techniques, field measurements and techniques like hydroacoustic measurements or piston core analysis were used to obtain knowledge about the geological settings of the seeps. The remote sensing techniques either involved manual or semi-automatic image analysis. An automatic algorithm that could quantitatively and qualitatively estimate the locations of oil seeps around the world would reduce the time and costs involved by a considerable margin. Synthetic Aperture Radar (SAR) sensors provide an illumination and weather independent source of ocean images that can be used to detect offshore oil seeps. Oil slicks on the ocean surface dampen the small wind driven waves present on the ocean surface and appear darker against the brighter ocean surface. They can, hence, be detected in SAR image. With the launch of the latest Sentinel-1 satellite aimed at providing free SAR data, an algorithm that detects oil slicks and estimates seep location is very beneficial. The global data coverage and the reduction of processing times for the large amounts of SAR data would be unmatchable. The aim of this thesis was to create such an algorithm that could automatically detect oil slicks in SAR images, map the location of the estimated oil seeps and quantify their seepage fluxes. The thesis consists of three studies that are compiled into one of more manuscripts that are published, accepted for publication or ready for submission. The first study of this thesis involves the creation of the Automatic Seep Location Estimator (ASLE) which detects oil slicks in marine SAR images and estimates offshore oil seepage sites. This, the first fully automatic oil seep location estimation algorithm, has been implemented in the programming language Python and has been tested and validated on ENVISAT images of the Black Sea. The second study reported in this thesis focuses on the optimisation of the created ASLE and comparison of the ASLE with other existing algorithms. It also describes the efficiency of the ASLE with respect to other existing algorithms and the results show that the ASLE can successfully detect seeps of active seepages. The third study aimed to provide the status of the offshore seepage in the southern Gulf of Mexico estimated from the ASLE using SAR images from ENVISAT and RADARSAT-1. The ASLE was used to detect natural oil slicks from SAR images and estimate the locations of feeding seeps. The estimated seep locations and the slicks contributing to these estimations were then analysed to quantify their seepage fluxes and rates. The three case studies illustrate that an automatic offshore seepage detection and estimation system such as the Automatic Seep Location Estimator (ASLE) is very beneficial in order to locate global oil seeps and estimate global seepage fluxes. It provides a technique to detect offshore seeps and their seepage fluxes in a fast and highly efficient manner by using Synthetic Aperture Radar images. This allows global accessibility of offshore oil seepage sites. The availability of large amounts of historic SAR datasets, the presence of 5 active SAR satellites and the latest launch of the European Space Agency satellite Sentinel-1, which provides free data, shows that there is no shortage in the availability of SAR data. The result of the work done in this thesis provides a means to utilise this large SAR dataset for the purpose of offshore oil seepage detection and offshore seepage related geophysical applications. The created system will be an important tool in the future not just to estimate offshore seepage in local seas but in global oceans that are otherwise challenging for field analysis

Satellite measurement of ocean turbulence

Turbulence and mixing in the surface layer of the ocean is a significant element in the combined ocean-atmosphere system, and plays a considerable role in the transfer of heat, gas and momentum across the air-sea boundary. Furthermore, improving knowledge of the evolution of energy within the ocean system, both globally and locally, holds importance for improving our understanding of the dynamics of the ocean at large- and small-scales. As such, insight into turbulence and turbulent flows at the ocean surface is becoming increasingly important for its role in ocean-atmosphere exchange and, from a wider perspective, climate change.A research project was initiated to understand the role that spacecraft remote-sensing may play in improving observation of “turbulence” (in a broad sense) in the ocean, and for identifying how steps towards such observation may be made. An initial, exploratory study identified the potential benefit of Synthetic Aperture Radar in “bridging the gap” between in-situ and remote observations o