Ship Wake Detection in SAR Images via Sparse Regularization

In order to analyse synthetic aperture radar (SAR) images of the sea surface, ship wake detection is essential for extracting information on the wake generating vessels. One possibility is to assume a linear model for wakes, in which case detection approaches are based on transforms such as Radon and Hough. These express the bright (dark) lines as peak (trough) points in the transform domain. In this paper, ship wake detection is posed as an inverse problem, which the associated cost function including a sparsity enforcing penalty, i.e. the generalized minimax concave (GMC) function. Despite being a non-convex regularizer, the GMC penalty enforces the overall cost function to be convex. The proposed solution is based on a Bayesian formulation, whereby the point estimates are recovered using maximum a posteriori (MAP) estimation. To quantify the performance of the proposed method, various types of SAR images are used, corresponding to TerraSAR-X, COSMO-SkyMed, Sentinel-1, and ALOS2. The performance of various priors in solving the proposed inverse problem is first studied by investigating the GMC along with the L1, Lp, nuclear and total variation (TV) norms. We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection. The results show that our proposed technique offers the best performance by achieving 80% success rate.Comment: 18 page

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

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 surface currents derived from Sentinel-1 SAR Doppler shift measurements

Reliable information about ocean surface currents is crucial for operational oceanography, regulating weather development, and climate research (e.g., UN SDG 13). Upper-ocean currents are also key for monitoring life below water, including conservation of marine biodiversity at every trophic level (e.g., UN SDG 14). Locating upper ocean currents “with the right strength at the right place and time” is moreover critically needed to support the maritime transport sector, renewable marine energy, and maritime safety operations as well as for monitoring and tracking of marine pollution. In spite of this, upper ocean currents and their variability are mostly indirectly estimated and often without quantitative knowledge of uncertainties. In this thesis, Sentinel-1 Synthetic Aperture Radar (SAR) based Doppler frequency shift observations are examined for the retrievals of ocean surface current velocity in the radar line-of-sight direction. In the first study (Paper 1), Sentinel-1 A/B Interferometric Wide (IW) data acquired along the northern part of the Norwegian coastal zone from October-November 2017 at a spatial resolution of 1.5 km are compared with independent in-situ data, ocean model fields, and coastal High-Frequency Radar observations. Although only a limited dataset was available, the findings and results reveal that the strength of the meandering Norwegian Coastal Current derived from the SAR Doppler frequency shift observations are consistent with observations. However, limitations are encountered due to insufficient calibration and lack of ability to properly partition the geophysical signals into wave and current contributions. A novel approach for calibration of the attitude contribution to the Sentinel-1B Wave Mode (WV) Doppler frequency shift emerged for a test period in December 2017 - January 2018. Building on this calibrated dataset, an empirical model function (CDOP3S) for prediction of the sea state-induced contribution to the Doppler shift observations is developed for the global open ocean in Paper 2. The assessment against collocated surface drifter data are promising and suggest that the Sentinel-1B WV acquisitions can be used to study the equatorial ocean surface currents at a monthly timescale with a 20 km spatial resolution. The calibrated dataset combined with the new geophysical model function developed in Paper 2 also allowed for the study (Paper 3) of ocean surface current retrievals from the high-resolution Sentinel-1B IW swath data acquired along the coastal zone on northern Norway. In this case, the geophysical model function had to be trained and adjusted for fetch limited coastal sea state conditions. The results demonstrate that the Sentinel-1B SAR-derived ocean surface currents significantly improved, compared to the findings reported in Paper 1. Although the thesis builds on a limited period of observations, constrained by the availability of experimental attitude calibration, the results are all in all promising. Reprocessing of the full Sentinel-1 A/B SAR Doppler shift dataset using the novel attitude bias correction is therefore strongly recommended for further improvement of the empirical model function. Regular use of the Sentinel-1 A/B SAR for ocean surface current monitoring would thus be feasible, leading to advances in studies of upper ocean dynamics in support to the Copernicus Marine Environment Monitoring Service (CMEMS) program and the United Nations (UN) Decade of Ocean Sciences.Doktorgradsavhandlin

On Small Satellites for Oceanography: A Survey

The recent explosive growth of small satellite operations driven primarily from an academic or pedagogical need, has demonstrated the viability of commercial-off-the-shelf technologies in space. They have also leveraged and shown the need for development of compatible sensors primarily aimed for Earth observation tasks including monitoring terrestrial domains, communications and engineering tests. However, one domain that these platforms have not yet made substantial inroads into, is in the ocean sciences. Remote sensing has long been within the repertoire of tools for oceanographers to study dynamic large scale physical phenomena, such as gyres and fronts, bio-geochemical process transport, primary productivity and process studies in the coastal ocean. We argue that the time has come for micro and nano satellites (with mass smaller than 100 kg and 2 to 3 year development times) designed, built, tested and flown by academic departments, for coordinated observations with robotic assets in situ. We do so primarily by surveying SmallSat missions oriented towards ocean observations in the recent past, and in doing so, we update the current knowledge about what is feasible in the rapidly evolving field of platforms and sensors for this domain. We conclude by proposing a set of candidate ocean observing missions with an emphasis on radar-based observations, with a focus on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

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

Developing a remote sensing system based on X-band radar technology for coastal morphodynamics study

New data processing techniques are proposed for the assessment of scopes and limitations from radar-derived sea state parameters, coastline evolution and water depth estimates. Most of the raised research is focused on Colombian Caribbean coast and the Western Mediterranean Sea. First, a novel procedure to mitigate shadowing in radar images is proposed. The method compensates distortions introduced by the radar acquisition process and the power decay of the radar signal along range applying image enhancement techniques through a couple of pre-processing steps based on filtering and interpolation. Results reveal that the proposed methodology reproduces with high accuracy the sea state parameters in nearshore areas. The improvement resulting from the proposed method is assessed in a coral reef barrier, introducing a completely novel use for X-Band radar in coastal environments. So far, wave energy dissipation on a coral reef barrier has been studied by a few in-situ sensors placed in a straight line, perpendicular to the coastline, but never been described using marine radars. In this context, marine radar images are used to describe prominent features of coral reefs, including the delineation of reef morphological structure, wave energy dissipation and wave transformation processes in the lagoon of San Andres Island barrier-reef system. Results show that reef attenuates incident waves by approximately 75% due to both frictional and wave breaking dissipation, with an equivalent bottom roughness of 0.20 m and a wave friction factor of 0.18. These parameters are comparable with estimates reported in other shallow coral reef lagoons as well as at meadow canopies, obtained using in-situ measurements of wave parameters.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic