Academic literature on the topic 'Marine radar images'

The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.

To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall. However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV). Then, an unsupervised K-means clustering learning method is used to obtain the clustering centers. Based on the constituted CCFV and the K-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by 10.0%, 6.3%, 2.0% and 0.6%, respectively, for the proportion of the 25% training dataset.

Cetaceans are protected species all over the world, most of them are vulnerable, endangered, or data deficient (according to International Union for Conservation of Nature - IUCN red list). X-band radars detect the echo of the electromagnetic signal reflected by an obstacle or a ship (target). The application of X-band radar to the detection of cetaceans is a new and innovative field of research that could improve the automation of marine mammal data collection, and this is the first time in the Mediterranean Sea. The aim of this work was to test the capability of X-band radar installed along the coast (ground-based) to detect and track cetaceans in a range of approximately 2.5 nautical miles from the radar antenna. Data collection included a part of field work, implemented through the acquisition of photographic images and target’s radar detection (by the panoramic terrace Santa Maria in Corniglia), and a part, performed in the laboratory, of data analysis. The work was undertaken between May and November 2018. During this period, 30 days of monitoring were carried out (about 300 h) and about 10,000 radar images were recorded. The first results showed that we were able to recognize the target “cetacean” from the other common targets (boats, buoys, etc.) detected by the radar. In particular 70 dolphins were sighted by visual census; 12 of them were recognized on radar images. Radar images allowed extraction of dolphin dive time (between 2 and 15 s). The next step will be to allow the radar to identify the presence of marine mammals itself since it also works at night and with low visibility. This technique could complement the protection measures of cetaceans, highlighting their presence at sea even if it is impossible with waves higher than 0.8 m and over distances greater than 2.5 km.

After decades of research, X-band marine radars have been broadly used for wind measurement. For retrieving the wind direction based on the wind-induced streaks, a lot of effort has been expended on three celebrated approaches—the local gradient method (LGM), the adaptive reduced method (ARM), and the energy spectrum method (ESM). This paper presents a scientific study of these methods. The contrast of retrieving the real measured marine radar images and vane measured results is evaluated, in perspective of the error statistics and algorithm operation efficiency. Interference factors, such as the historical information of the measured area, reference wind speed, and sea condition showing in the monitoring equipment are also concerned. The tentative results showed that LGM is robust, which can be implemented in most radar images, because it allows for a lower selection of requirements compared with the other two methods. For ARM, the better retrieval performance is a tradeoff with extra computation, which is expensive. ESM is superior to the other two algorithms in terms of accuracy and computation load; however, this algorithm is sensitive in rain-contaminated radar images, meaning it is a good choice for data post-processing in the lab.

Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.

The significant wave height (SWH) retrieved from collocated HH and VV polarized X-band marine radars under different sea states is studied. The SWH are retrieved from different principal components of X-band marine radar image sequence. As compared with the SWH measured by a buoy, the root-mean-square errors of the SWH are 0.32–0.45 m for VV polarization, and they are 0.37–0.60 m for HH polarization. At the wind speeds of 0–5 m/s, the SWH can be derived from VV polarized radar images, while the backscatter of HH polarized radar is too weak to contain wave signals at very low wind speeds (~0–3 m/s). At the wind speeds of 5–18 m/s, the SWH retrieved from VV polarization coincide well with the SWH measured by the buoy, while the SWH retrieved from HH polarization correspond with the changes of the wind speed. At the wind speeds of 18–26 m/s, the influence of wave breaking on HH polarization is more important than that on VV polarization. This indicates that the imaging mechanisms of HH polarized X-band marine radar are different from those of VV polarized X-band marine radar.

This research proposes a simultaneous localization and mapping approach to obtain the positioning information of a vessel in accordance with sequential radar images. At the very beginning, the digital image preprocessing methods are used to obtain the static feature point in radar images. Subsequently, the trajectory of the vessel is calculated based on a simultaneous localization and mapping–based algorithm. Finally, the calculated vessel trajectory is compared with the actual trajectory to verify the validity of the proposed approach. With the help of this approach, marine radar is capable of providing temporal positioning information of the vessel from a plethora of blips captured in frame-by-frame radar images. The proposed approach is unique in that it used marine radar as the only sensor to obtain the positioning information of the vessel. Particularly, field testing has been conducted to validate the effectiveness and accuracy of the proposed approach.

Unmanned surface vehicles (USV) have become an intense research area because of their extensive applications. Marine radar is the most important environmental perception sensor for USV. Aiming at the problems of noise jamming, uneven brightness, target lost in marine radar images, and the high-speed USV to the requirement of real-time and reliability, this paper proposes the radar image target detection algorithms which suitable for embedded marine radar target detection system. The smoothing algorithm can adaptive select filter in noise, border and background areas, improves the efficiency and smoothing effect. Based on the iterative threshold, the tolerance coefficient is selected by the histogram, ensures the robust of segmentation algorithm. The location, area and invariant moments features can be extracted from the radar image which after connected-component labeling. The actual radar image processing results demonstrate the effectiveness of the proposed algorithms.

In this work, a new concentric circles detection method for object detection is proposed. It has been applied to the images of a commercial radar, captured with a Charge-Coupled Device (CCD) camera. The processing includes the detection of centres and concentric circles in the images and the identification of the radar scale. Several methods found in the literature have been applied and compared with our novel proposal for multiple concentric circles detection, called “Propagation Method based on Circular Regression”. This methodology has been validated with real radar images, proving its efficiency in obtaining the distance of any object to a marine vessel, with high accuracy and low computational cost, in real time. This system can not only be applied to most existing radars in the market by adjusting the parameters of each model but our proposal for concentric circle detection can be also applied to other sensing applications.

Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film detection in ship-borne radar images using pixel area threshold parameters. After applying a pre-processing scheme with a Laplace operator, an Otsu threshold, and mean and median filtering, the shape and area of the oil film can be calculated rapidly. Compared with other ACMs, the improved Local Binary Fitting (LBF) model is robust and has a fast calculation speed for uniform ship-borne radar sea clutter images. The proposed method achieves better results and higher operation efficiency than other automatic and semi-automatic methods for oil film detection in ship-borne radar images. Furthermore, it provides a scientific basis to assess pollution scope and estimate the necessary cleaning materials during oil spills.

This thesis describes research performed into the segmentation and classification of features on images of wound terrain generated from an airborne millimetre wave radar. The principles of operation of the radar are established and it is shown how an image is produced from this particular radar. The parameters such as wavelength, antenna size and pulse length are related to the images and a mathematical description of the radar data is given. The effectiveness of established image processing techniques is reviewed when applied to millimetre wave radar images and a statistical classification technique is seen to yield encouraging results. This method of segmentation and classification is then extended to make optimal use of the available information from the radar. An orthogonal expansion of the Poincaré sphere representation of polarised radiation is established and it is shown how different terrain types cluster in the eigenspace of these spherical harmonics. Segmentation then follows from the clustering properties of pixels within this multidimensional eigenspace and classification from the locations of the clusters.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 103-105).
The X-band marine radar is one type of remote sensing technology which is being increasingly used to measure sea surface waves nowadays. In this thesis, how to reconstruct sea surface wave elevation maps from X-band marine radar images and do wave field prediction over short term in real time are discussed. The key idea of reconstruction is using dispersion relation based on the linear wave theory to separate the wave-related signal from non-wave signal in radar images. The reconstruction process involves three-dimensional Fourier analysis and some radar imaging mechanism. In this thesis, an improved shadowing simulation model combined with wave field simulation models for the study of the correction function in the reconstruction process and an improved wave scale estimation model using non-coherent radar data are proposed, which are of great importance in the reconstruction process. A radar image calibration method based on wave field simulation is put forward in order to improve the quality of reconstructed sea surface wave. Besides, a theoretical wave scale estimation model using Doppler spectra of the coherent radar is put forward, which is proposed to be a good alternative to the current wave scale estimation model. The reconstructed sea surface wave can be used for wave field simulation in order to predict the wave field, which is not only an application of this reconstruction process, but also a parameter optimizing tool for the reconstruction process.
by Yusheng Qi.
S.M.

Simultaneous Localization And Mapping (SLAM) is a process of mapping an unknown environment and at the same time keeping track of the position within this map. In this theses, SLAM is performed in a marine environent using radar images only.

A SLAM solution is presented. It uses SIFT to compare pairs of radar images. From these comparisons, measurements of the boat movements are obtained. A type of Kalman filter (Exactly Sparse Delayed-state Filter, ESDF) uses these measurements to estimate the trajectory of the boat. Once the trajectory is estimated, the radar images are joined together in order to create a map.

The presented solution is tested and the estimated trajectory is compared to GPS data. Results show that the method performs well for at least shorter periods of time.

Plus de la moitié du transport de marchandises à travers le monde se fait par voie de mer. Un des moyens permettant d’assurer la sécurité des navires et par conséquent d’éviter les pollutions accidentelles est de mettre à la disposition des marins des cartes marines de qualité, conformes aux moyens modernes de navigation (navigation par GPS). Malheureusement, actuellement, ces documents de navigation sont anciens sur nombre de zones côtières. Le SHOM qui est chargé en France de l’établissement et de la diffusion de l’information nautique utilise, dans certains cas, pour actualiser plus rapidement ses cartes des images de télédétection provenant des satellites optiques SPOT. Dans les zones à forte nébulosité, l’acquisition de telles données est très aléatoire, les images des satellites RADAR à Synthèse d’Ouverture (RSO) apportent l’avantage de ne pas être perturbées par la couverture nuageuse. L’étude que nous présentons, née de la collaboration entre le SHOM et l’UMLV, a pour objectif de développer une méthode d’utilisation des données spatiales radar en vue de faciliter la création et la réactualisation des cartes marines. Le premier chapitre de ce travail présente, sur chacun des différents sites qui vont être « auscultés » au travers des images radar à synthèse d’ouverture, les grandes caractéristiques du milieu, la base de données images et cartes puis l’intérêt du site pour la cartographie marine. Les cinq sites côtiers présentés sont en partie en milieu tropical humide (la Guyane française, le Cameroun et le Gabon), en milieu polaire (Terre Adélie) et enfin en milieu inter-tropical aride (Mauritanie). Le deuxième chapitre porte sur les traitements radiométriques et géométriques mis en œuvre pour l’utilisation des images radar. Les traitements radiométriques comporteront principalement l’usage d’algorithmes connus et de logiciels déjà existants pour le filtrage du chatoiement sur les images d’amplitude, la génération des images de cohérence à partir de données complexes et l’extraction automatique d’informations. En ce qui concerne les traitements géométriques, nous avons choisi de développer un outil de géoréférencement pour les scènes ERS. Cet outil demande de connaître l’orbitographie du satellite, la position du géoïde sur le site imagé et la géométrie interne des données satellitaires. La validation de cet outil s’appuiera sur la prise en compte de points de contrôle au sol et sur la comparaison d’images acquises en orbites montante et descendante. L’analyse des images traitées se fera sur les thèmes intéressant la cartographie marine : en mer, au large et sur les zones de petits fonds proches de la côte, sur l’estran, et enfin, à terre, sur les végétations côtières et les zones anthropisées. D’autres données de télédétection (images optiques et autres données RSO) pourront être utilisées, selon les disponibilités, pour enrichir notre propos. Enfin le dernier chapitre est consacré, d’une part, aux réactualisations de cartes marines auxquelles nous avons participé et d’autre part, à la proposition de produits cartographiques nouveaux fusionnant les images RSO récentes et des données cartographiques plus anciennes, destinés à suppléer à l’absence de cartes classiques actualisées et susceptibles d’intégrer les évolutions observées sur certains sites d’étude
More half of the carriage of goods throughout the world is done by sea. One of the means allowing to ensure the safety of the ships and consequently to avoid accidental pollution, is to put at provision of the sailors of the sea charts of quality, in conformity with the modern means of navigation (navigation by GPS). Unfortunately, currently, these documents of navigation are old on a number of coastal zones. The SHOM who is in charge in France of the establishment and of the diffusion of nautical information, uses, in certain cases, to bring up to date its cards more quickly, of the images of remote sensing coming from the optical satellites SPOT. In zones with strong nebulosity, the acquisition of such data is very random, the images of the satellites with Synthetic Aperture RADAR (SAR) bring the advantage of not being disturbed by the cloud cover. Present study is borned of collaboration between SHOM and UMLV, its main goal is to develop a method to use SAR images in order to facilitate nautical chart reactualisation. First chapter of this work presents, on each various site which "will be auscultated" through images radar with synthesis of opening, great characteristics of the medium, the data base images and cards then interest of the site for marine cartography. The five coastal sites presented are partly in humid tropical area (French Guiana, Cameroon, Gabon), in polar zone (Terre-Adélie) and last in arid inter-tropical area (Mauritania). Second chapter relates to the radiometric and geometrical processing implemented for the use of the images radar. Radiometric processing will comprise mainly the use of known algorithms for filtering of the speckle on amplitude images, generating coherence images with complex SAR data and extracting automatically information from SAR data. With regard to the geometrical processing, we chose to develop a tool in order to georeference our ERS images database. This tool requires to know satellite orbitography, geoid height on study site and internal satellite geometry. Validation of this tool will be based on ground control points acquisition and on comparison between ERS images acquired in ascending and descending pass. SAR data analysis will be done on topics interesting nautical charts updating: shallow waters close to coast, on inter-tidal zone, and finally, on terrestrial part, coastal vegetation and anthropic zones. Other remote sensing data (optical and different SAR data) could be used, according to availabilities, to enrich our matter. Last, fourth chapter is devoted with re-actualisations of nautical charts in which we participated. We propose new cartographic products including recent SAR images and older data from nautical charts, to compensate lack of traditional maps up to date and likely to integrated evolutions observed study sites

Nous nous intéressons au problème d’extraction du fouillis de mer dans des images radar marin. Le parti pris est de développer des méthodes de traitement d’image permettant de s’affranchir au mieux d’hypothèses sur la nature du fouillis de mer et du signal d’intérêt. D’une part, nous proposons un algorithme basé sur une approche variationnelle originale : un modèle multiphasique à interface diffuse. Les résultats obtenus montrent que l’algorithme est efficace lorsque le signal d’intérêt a un rapport signal-sur-fouillis suffisamment grand. D’autre part, nous nous intéressons à l’implémentation de schémas de Boltzmann sur réseau pour des problèmes de convection-diffusion à vitesse d’advection non constante et un terme source non nul. Nous décrivons le calcul de la consistance obtenue par analyse asymptotique à l’échelle acoustique et avec un opérateur de collision à temps de relaxation multiples, et étudions la stabilité de ces schémas dans un cas particulier. Les résultats obtenus montrent que les schémas proposés permettent de supprimer le bruit résiduel et de renforcer le signal d’intérêt sur l’image obtenue grâce à la première méthode. Enfin, nous proposons une méthode d’apprentissage permettant de s’affranchir d’hypothèses sur la nature du signal d’intérêt. En effet, en complément de l’algorithme par approche variationnelle, nous proposons un algorithme basé sur le traitement pulse-Doppler lorsque le signal d’intérêt est exo-clutter et a un rapport signal-sur-fouillis faible. Les résultats obtenus à partir du double auto-encodeur que nous proposons, étant comparables aux résultats fournis par chacune des deux méthodes, permettent de valider cette approche
We focus on the problem of sea clutter extraction in marine radar images. The aim is to develop image processing methods allowing us to avoid assumptions about the nature of the sea clutter and the signal of interest. On the one hand, we propose an original algorithm based on a variational approach : a multiphase model with diffuse interface. The results obtained show that the algorithm is efficient when the signal of interest has a sufficiently large signal-to-clutter ratio. On the other hand, we focus on the implementation of lattice Boltzmann schemes for convection-diffusion problems with non-constant advection velocity and non-zero source term. We describe the computation of the consistency obtained by asymptotic analysis at the acoustic scale and with a multiple relaxation time collision operator, and study the stability of these schemes in a particular case. The obtained results show that the proposed schemes allow removing the residual noise and to enhance the signal of interest on the image obtained with the first method. Finally, we propose a learning method allowing us to avoid assumptions on the nature of the signal of interest. Indeed, in addition to the variational approach, we propose an algorithm based on pulse-Doppler processing when the signal of interest is exo-clutter and has a low signal-to-clutter ratio. The results obtained from the proposed double auto-encoder, being comparable to the results provided by each of the two methods, allow validating this approach

Els oceans mantenen una fràgil i complexa cadena que enllaça un alt nombre de factors biològics, sociològics i econòmics. Actualment, aquest ecosistema està amenaçat per l'activitat humana i uns dels punts més crítics és la sobreexplotació pesquera. Això ha despertat la consciencia de les autoritats d'arreu per a protegir l'entorn marí i assegurar, així, la seguretat i supervivència dels éssers humans. Tal objectiu demana el desenvolupament de polítiques de control que monitorin l'activitat dels vaixells.

Fins l'actualitat, diferents propostes s'han estudiat per monitorar vaixells, com per exemple transpondedors, teledetecció òptica i sensors acústics passius. L'experiència en entorns reals ha demostrat que cap d'aquestes solucions és eficient. Una alternativa poden ser els Radars d'Obertura Sintètica (SAR). Aquests sistemes utilitzen les propietats de reflectivitat i dispersió dels vaixells per identificar-los amb independència de qualsevol fenomen atmosfèric i del cicle dia/nit. El sensors SAR sintetitzen una obertura més gran que la real permetent l'obtenció d'imatges de reflectivitat d'uns quants kilòmetres d'amplada amb una resolució de pocs metres.

En la monitorització de vaixells, la tecnologia SAR ha demostrat unes bones prestacions per la detecció. Treu profit del fet que els vaixells dispersen més energia que el mar i, així, apareixen en les imatges com punts molt brillants. Però, la seva utilitat en la identificació de vaixells encara no està clara. Hi ha dues limitacions importants: 1) les resolucions dels sistemes actuals no semblen suficients per aïllar característiques geomètriques a partir de la informació de reflectivitat i 2) les distorsions que les signatures dels vaixells experimenten en entorns marins. Aquests problemes es poden resoldre parcialment si s'utilitzen dades SAR multidimensional. Aquest concepte es refereix al fet d'adquirir imatges SAR modificant un o més paràmetres del sistema. En la classificació de vaixells, hi ha dues opcions clares: 1) Polarimetria SAR (PolSAR) que utilitza les dues components polarimètriques de l'ona EM i 2) la Interferometria SAR que s'obté per la combinació de dues imatges SAR adquirides des de posicions molt properes. Per a una banda, la polarització de l'ona EM és una propietat intrínseca de l'ona que ajuda a aïllar estructures geomètriques particulars per mitjà de la teoria de descomposició de blancs (TD). Per l'altra, la interferometria treu profit de la diferencia de fase entre les dues imatges SAR per obtenir la tercera dimensió de l'escena.

PolSAR and InSAR presenten grans possibilitats per la monitorització de vaixells ja que poden solucionar algunes de les limitacions dels mètodes clàssics. Desafortunadament, encara no han estat profundament estudiades a causa de les dificultats en obtenir dades reals validades. Això ha limitat el nombre d'estudis en aquesta temàtica. En aquest entorn, la tesi està orientada a avaluar fins a quin punt les tècniques PolSAR i InSAR poden ser útils per la monitorització de vaixells. Per a tal propòsit, s'han fixat quatre objectius importants:
1. El desenvolupament d'un simulador SAR eficient que doni imatges realistes de vaixells i que solucioni el dèficit de dades reals en entorns marins.
2. L'estudi de la dispersió dels vaixells que fixi els principals mecanismes de dispersió observats en imatges SAR i com es relacionen amb la geometria dels vaixells.
3. Un estudi de les prestacions de les tècniques actuals d'anàlisis de dades PolSAR en la classificació de vaixells.
4. El desenvolupament d'un mètode nou i eficient per la identificació de vaixells.

Al llarg de la tesis, els diferents punts seran estudiats i resolts. El desenvolupament de GRECOSAR, un simulador SAR de blancs complexes que dóna imatges de vaixells similars a les adquirides en entorns reals, ha estat essencial per estudiar les propietats de dispersió dels vaixells. Ha permès demostrar que els vaixells es poden distingir a partir del seu patró dispersiu, el qual és senzill i dominat per alguns dispersors guia que presenten una marcada estabilitat i potència de dispersió. Amb aquests resultats ha estat possible desenvolupar un nou mètode que pot identificar vaixells sota condicions d'observació adverses. Combina característiques polarimètriques i interferomètriques SAR (PolInSAR) per inferir estimacions 3D de la geometria dels vaixells. Diferents tests han demostrat que aquest mètode dóna una millor fiabilitat en la identificació que altres mètodes actualment disponibles. Malgrat tot, fixa uns requeriments tecnològics més elevats, sobretot en la resolució de les imatges i en les característiques PolInSAR. La nova generació de sensors SAR els poden cobrir.
Oceans support a complex and fragile chain that links a high number of biological, sociological and economical factors. In these days, this ecosystem is endangered by human activity and one of the main hot spots is overfishing. As a result, authorities worldwide have become aware about the necessity to law-protect the marine environment in order to assure the safety and sustenance of human beings. This demands the development of fisheries policy to monitor the activities of ships.

Up to now, different vessel monitoring proposals have been considered, for instance transponders, optical remote sensing or passive acoustic sensors. The lessons learnt in real scenarios have shown that none of these solutions is efficient. A feasible option may be the so-called active Synthetic Aperture Radar (SAR) technology. It uses the reflectivity/scattering properties of vessels for basing the identification process with independence of any atmospheric phenomena and day/night cycle. SAR sensors synthesize an antenna aperture larger than the real one and this allows to acquire reflectivity images of some tens of kilometers wide with a resolution of few meters.

In vessel monitoring, SAR imagery has proven good performance for vessel detection. They take profit of the fact that vessels normally backscatter more power than the sea and, hence, they appear in the images as bright spots. But their usefulness in vessel identification has not been established yet. There are two main limitations, namely: 1) the resolution of current systems that appears to be not enough for isolating geometrical features from the reflectivity information of SAR images and 2) the distortions that vessel' signatures experiment within sea scenarios. Such problems can be solved up to certain extend if multidimensional SAR data is used. This concept refers to the possibility to acquire different SAR images by modifying one or more imaging parameters. In the scope of vessel classification, there are two main options, namely: 1) SAR polarimetry (PolSAR) that refers to the usage of the two polarimetric components of the EM wave and 2) SAR interferometry (InSAR) derived by combining two SAR images acquired from slightly different positions. On the one hand, the polarization of an EM wave is an intrinsic wave property that helps on identifying specific geometrical structures via Target Decomposition (TD) theory. On the other hand, Interferometry takes profit of the phase difference between the two SAR images to retrieve the third dimension of the scene.

PolSAR and InSAR have great potentialities for supporting vessel monitoring as they can overcome some of the limitations of classical methods. Unfortunately, they have not been exploited yet due to the difficulties on having at one's disposal real data with reliable ground-truth. This has limited the number of works tackling such issue. In this framework, the current thesis is focused to evaluate up to which extend PolSAR and InSAR imagery are reliable for vessel monitoring. For such purpose, four main goals are proposed, namely:
1. The development of an efficient SAR simulation environment that provides realistic vessel SAR images and overcomes the current data deficiency related to marine scenarios.
2. The study of vessel scattering to fix the main polarimetric scattering mechanisms observed in vessel SAR images and how they are related with the geometries of vessels.
3. A performance study of current analysis tools of PolSAR data in vessel classification.
4. The development of a novel and efficient methodology for vessel identification.

Along the thesis, the different points are studied and solved. The development of GRECOSAR, a SAR simulator of complex targets able to provide vessel images similar to those obtained in real scenarios, has been essential for studying the scattering properties of vessels. It has allowed to show that vessels can be distinguished by means of their scattering pattern, which appear to be not so complex and dominated by some guide scatters that present a marked reflectivity stability and scattered power. With these results, a new approach able to identify vessels even under adverse observation conditions has been developed. It combines polarimetric and interferometric SAR (PolInSAR) capabilities to retrieve 3D estimates of the geometry of ships. Different tests have shown that the proposed method provides better identification confidence than other available methods. However, it demands higher technological requirements in terms of image resolution and PolInSAR features. The new generation of SAR sensors may fulfill them.

Dans l’océan, les bancs de proies représentent une source d’alimentation ponctuelle. Pour pallier à cela, les oiseaux marins profitent de l’information sociale provenant de conspécifiques, hétérospecifiques, ou bateaux de pêche. Dans cette thèse, nous cherchons à évaluer l’influence de différentes sources d’information sur les stratégies de recherche alimentaire des oiseaux marins en milieu pélagique tropical.Les oiseaux détectent des prédateurs ou des bateaux à des distances dépassant dix kilomètres. Nous étudions donc leur distribution à sub-méso-échelle, ceci grâce à des images d’un radar à bord d’un thonier senneur.Les images radar offrent une visualisation, dans les 30 km autour du navire, de la distribution instantanée de la communauté d’oiseaux en vol et de leurs déplacements. Nous mettons au jour des motifs d’agrégation temporaires, d’une dizaine de kilomètres de diamètre, au sein de la distribution des oiseaux. Les distances entre groupes d’oiseaux sont compatibles avec l’échange d’information au sein de ces agrégations.Une étude plus fine révèle des vols coordonnés de groupes d’oiseaux distants de plusieurs centaines de mètres («râteaux»), suggérant un haut degré de coopération lors de la recherche de proies. Nous questionnons ensuite le degré de perturbation du senneur dans la recherche alimentaire des oiseaux. Les résultats indiquent que les oiseaux marins discriminent les différentes activités du navire.Ce travail constitue, à notre connaissance, la première utilisation des radars embarqués pour l’étude de l’écologie des oiseaux en haute mer, et ouvre nombre de perspectives pour comprendre la dynamique des interactions entre prédateurs marins supérieurs
In the ocean, prey is patchily distributed. To overcome this challenge, pelagic seabirds benefit from social information from conspecifics, heterospecifics, or fishing boats.As part of this dissertation, we aim at evaluating the influence of different information sources in seabird foraging strategies in the tropical pelagic environment.Seabirds can detect visible predators or boats at distances of over ten kilometres. We thus study the distribution of seabirds at sub-meso-scale, analysing images recorded from a radar on board a tuna purse-seiner. Radar images provide a visualisation of the instantaneous distribution of the in-flight seabird community as well as seabird movements within thirty kilometres of the seiner. We detect over-aggregatedand temporary patterns, spanning about ten kilometers, within flying seabirds’ distribution. Distancesbetween seabird groups are compatible with information exchanges within these aggregations. A finer scale study reveals coordinated flights of seabird groups distant of hundreds of meters from each others (« rakes »), and suggesting a high level of coordination during foraging. We then investigate the potential disturbance induced by the seiner in the seabirds’ foraging network.First results indicate that seabirds can discriminate the different seiner’s activities.To our knowledge, this work is the first contribution of on board radars use for the study of seabird ecology in the high seas, and opens interesting perspectives, such as the understanding of interactions’ dynamics within the marine top-predators guild

Synthetic aperture radar imaging is an effective tool for imaging the sea surface because of its response to changes in sea surface roughness. This allows for the remote sensing of features on the sea surface, which modulate se surface roughness. In this work, 18 synthetic aperture radar images were collected from the TerraSAR-X and RADARSAT-2 satellites in the Port Everglades, Florida area. In-situ measurements were collected in conjunction with the satellite images in order to provide more information on the features visible in the imagery, and aid in identification of the origin of the features. Information on ships in the area of the satellite image footprints was collected using an automatic information system. Weather conditions were recorded by a meteorological station and a National Oceanic and Atmospheric Administration weather radar station. Waves and currents in the observational area were recorded with acoustic Doppler current profilers and wave gauges. Sonar systems and conductivity, depth, and salinity profilers were used to identify stratification in the water column. Surfactant release experiments were also conducted to explore the affects of surface active materials. Results of the experiment show the manifestation of atmospheric effects, oceanic fronts and eddies, wind shadowing, natural and artificial slicks, and ships and ship wakes on the synthetic aperture radar imagery. Atmospheric conditions were found to play a significant role in the visibility of features on the sea surface, and sometimes masked the appearance of features on the ocean surface. Overall the most reliable feature capable of being imaged on the sea surface by the synthetic aperture radar satellites was the signatures of ships and their wakes.

Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.

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.

A high-speed marine radar combined with advanced digital processing can produce high resolution images that allow the user to identify hazardous ice features and small icebergs which are nearly impossible to see with a conventional radar. The Canadian Coast Guard (CCG) is working in partnership with Transport Canada’s Transportation Development Centre (TDC) to develop an “Ice Hazard Radar”. The system was evaluated in three dedicated trials on board the icebreaker CCGS Henry Larsen to fully demonstrate the capability of the radar in a variety of operating environments and ice conditions. This paper presents the results of the trials.

The use of remotely wave sensing by a marine radar is increasingly needed to provide wave information for the sake of safety and operational effectiveness in many offshore activities. Reconstruction of radar images needs to be carried out since radar images are a poor representation of the sea surface elevation: effects like shadowing and tilt determine the backscattered intensity of the images. In [1], the sea state reconstruction and wave propagation to the radar has been tackled successfully for synthetic radar images of linear seas, except for a scaling in the vertical direction. The determination of the significant wave height from the shadowed images only has been described in [2]. This paper will summarize these methods, and provides the first results for the extension to nonlinear seas.