AIS-based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

International audienceThis paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detec- tion algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (Automatic Identification System) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version

Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions : the Murray-Darling basin in Australia as a test case

The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray-Darling basin in Australia. The hydrometeorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010-2011). Next, SMOS brightness temperature observations are sequentially assimi-lated into the coupled SUPERFLEX-CMEM model (20102015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM). This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average

GFM Product User Manual

This Product User Manual (PUM) is the reference document for all end-users and stakeholders of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PUM provides all of the basic information to enable the proper and effective use of the GFM product and associated data output layers. This manual includes a description of the functions and capabilities of the GFM product, its applications and alternative modes of operation, and step-by-step guidance on the procedures for accessing and using the GFM product

Détection et caractérisation des navires sur images SAR spatiales et couplage avec des données coopératives de positionnement

SAR imagery presents an increased interest in maritime surveillance applications. The research work completed in this thesis is dedicated to vessels detection and signature characterization from data acquired by different spaceborne SAR sensors. Firstly, we assess the performances of different ship detectors based on adaptive threshold algorithms. The detection algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (automatic identification system) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations. Secondly, the effects of stationary-based processing of moving ship signatures in SAR imagery are assessed and a methodology that makes it possible to estimate and compensate them is introduced. SAR imaging of moving targets usually results in residual chirps in the azimuthal SLC processed signal. The Fractional Fourier Transform (FrFT) allows to represent the SAR signal in a rotated joint time¿frequency plane and performs an optimal processing and analyse of chirp signals. Employing the FrFT reduces the effects of residual chirps achieving compensation of the along-track defocus of a moving target and estimation of the target¿s azimuthal speed itself. Experiments are conducted on Radarsat-2 Multilook Fine and Ultrafine SAR images. Evaluation using a large number of ship signatures allows to assess the efficiency of the proposed method. Comparisons with AIS data as ground truth and with a method based on the assessment of the temporal correlation between a sequence of sublook images are carried out for a complete performance analysis. Finally, the use of complex dual-polarization data for SAR vessel detection is assessed. As a first step, an intercomparison between the individual use of each polarimetric channel is considered, as well as the fusion of the detection results corresponding to the two polarimetric channels. In a second phase, the fusion of both polarization channels before the detection step is assessed. When dealing with amplitude data only, we propose to employ a method based on the generalized temporal moments (Hölder means), in order to fuse the information of both polarization channels. When dealing with complex data, the coherence coefficient or target dual-polarimetric decompositions, which may provide additional information in comparison with single channel imagery, are employed.L'imagerie Radar à Synthèse d'Ouverture (RSO) est fréquemment utilisée dans les applications de surveillance maritime, notamment en raison de son indépendance à la lumière du jour, aux nuages ou aux conditions météorologiques. Le travail de recherche mené dans cette thèse est consacré à la détection de navires et à la caractérisation de leurs signatures à partir de données acquises par différents capteurs RSO. Premièrement, nous avons évalué les performances des différents algorithmes de détection de navires basés sur un seuillage adaptatif. Ces algorithmes de détection sont basés sur diverses distributions statistiques de "fouillis de mer" et sont automatiquement évalués. Une analyse prenant en entrée de nombreux jeux de données d'images RSO et de données AIS utilisées comme "vérité terrain", a permis d'évaluer l'efficacité de chaque algorithme. En fonction des jeux de données utilisés, les algorithmes de détection présentent différents avantages et inconvénients. Une méthode de couplage RSO-AIS a été utilisée pour discriminer les cibles réelles détectées des fausses alarmes et ce, afin de déterminer le taux de détection et d'effectuer une comparaison cohérente des différents algorithmes de détection. L'influence des caractéristiques des capteurs RSO (angle d'incidence, polarisation, fréquence et résolution spatiale) a été évaluée, ainsi que l'influence de la longueur des navires. Des expériences ont été menées sur des jeux de données Radarsat-2 et CosmoSkymed ScanSAR ainsi que sur des données AIS acquises depuis les stations côtières. Ensuite, les effets de l'hypothèse de la stationnarité lors du traitement de l'image RSO ont été évalués sur la signature des navires mobiles. Une méthodologie qui permet d'estimer et de compenser ces effets a alors été introduite. L'imagerie RSO des cibles mobiles se traduit généralement par des signaux résiduels de type "chirp" dans le signal complexe et dans la direction azimutale. La Transformée de Fourier Fractionnaire (FrFT) permet de représenter le signal RSO dans un domaine de rotation temps-fréquence, ce qui permet d'effectuer un traitement optimal des signaux de type "chirp". L'emploi de la FrFT réduit les effets de ces signaux résiduels de type chirp et permet de compenser la défocalisation azimutale d'une cible mobile ainsi que d'estimer la vitesse azimutale de la cible visée. Des expériences sur des données Radarsat-2 ont été menées et l'utilisation d'un grand nombre de signatures de navires a permis d'évaluer l'efficacité de la méthode proposée. Des comparaisons avec les données AIS ainsi qu'avec une méthode basée sur l'évaluation de la corrélation temporelle dans une séquence de sous-images ont été effectuées pour obtenir une analyse complète des performances. Enfin, l'utilisation de données à double polarisation dans la détection des navires RSO a été évaluée. Dans un premier temps, une comparaison des résultats de détection propres à chaque canal polarimétrique a été considérée, pour ensuite évaluer la fusion des résultats. Dans une deuxième phase, la fusion de ces deux canaux de polarisation avant l'étape de détection a été étudiée. Concernant la fusion des données en amplitude, nous avons proposé d'utiliser une méthode basée sur les moments temporels généralisés Hölder. Pour les données complexes, le coefficient de cohérence ou les décompositions polarimétriques ont été utilisés et ont pu fournir des informations supplémentaires par rapport à l'imagerie à un seul canal

Assimilation of probabilistic flood maps into large scale hydraulic models to retrieve missing river geometry data using a tempered particle filter

MO1.R7: Topographic and Hydrologic MappingInternational audienceAs climate change worsens, intensified natural events are expected to happen in the future. Among these events, floods can be the most destructive and can cause significant damages on many levels. It is therefore necessary to put in place cost-effective flood forecasting models in order to obtain accurate dynamic simulations for flood risk assessment. However, current models are affected with large uncertainties and need to be constrained with independent data that must be acquired from in situ measurements or remote sensing derived observations. If the network of hydrometric stations was well developed, flow rate and/or stage time series should be provided at a relatively sufficient spatial and temporal coverages and used as inputs for flood models. However, in many areas around the world, stream gauges are sparsely distributed and can be lacking in ungauged basins. To compensate the lack of in situ data, we propose to exploit earth observation (EO) and particularly make use of Synthetic Aperture Radar (SAR) imagery due to its ability to provide frequent updates of flooded areas at a large scale, regardless of atmospheric conditions. Moreover, the specular reflection of the emitted backscatter on open water bodies allows a relatively straightforward detection of water on the SAR image. Thus, these images hold an added value and a potential to improve the predictive accuracy of flood forecasting models through data assimilation (DA). Widely used in the fields of hydrology, hydraulics and geosciences, DA aims to optimally combine uncertain model predictions and uncertain observations. This relies on the estimation of optimal model states and/or parameters and allows thereby for the reduction of model uncertainties. DA can be carried out sequentially, for example in near-real time, by updating model states and/or parameters using observations as they become available, or in a reanalysis by assimilating all observations at once, i.e., retrospectively.Another major challenge tied to the hydrodynamic modelling is the lack of hydraulic parameter data that are needed as inputs, such as the riverbed shape and elevation. While the knowledge of such information is critical for flood models, it is rarely available from remote sensing observations, digital elevation models (DEMs), or ground data measurements. Most studies have estimated river discharges and depths assuming the bathymetry and bed roughness to be known a priori. The complexity of implementing DA to estimate these hydraulic parameters have long been seen has a pitfall.In this study, we propose to assimilate probabilistic flood maps derived from SAR data into the SW2D-DDP model, a 2-dimensional shallow water equations model with depth-dependent porosity, in order to retrieve the unknown bathymetry of a river. The porosity functions in this model, enable a straightforward representation of the riverbed geometry using porosity parameters. We assume the bed shape to be trapezoidal, and the bed roughness to be known a priori. The DA framework is thus based on integrating PFMs into the SW2D-DDP model via a Tempered Particle Filter (TPF) and takes into account the SAR observation and the SW2D-DDP model related uncertainties. The data assimilation algorithm is applied using as a test case the 2012 flood event that hits the Severn River around the city of Tewkesbury at the confluence of the Severn and Avon Rivers. We thus proposed to retrieve via data assimilation a simplified spatially distributed riverbed geometry (i.e., riverbed depth) along with the model downstream boundary condition in the form of a rating curve. The results are very encouraging as the model predictions reach water level RMSEs below 0.5 m as a result of the assimilation although the retrieved river depths are not matching the real one

Monitoring changes in the coastal environment based on SAR Sentinel-1 time-series

This research addresses the use of Sentinel-1 time series with the aim of detecting spatio-temporal changes in the coastal environment. To this end an automatic and unsupervised coastline detection method is proposed. First, we apply a temporal averaging filter that allows encapsulating the temporal variations in coastal areas, e.g. due to tides or vegetation, and at the same time it allows reducing the speckle, without decreasing the spatial resolution of the Synthetic Aperture Radar (SAR) images. Then, based on the distinctive backscattering values of the sea and land classes we employ an iterative hierarchical tiling method in order to accurately characterize the two classes by a bimodal distribution. The latter is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. The proposed method is applied to two different SAR time-series, each one acquired throughout one year. The extracted yearly coastlines are then analyzed in order to identify spatio-temporal changes. Experimental results showcase coastal area changes between occuring 2018 and 2019 and that were caused by the hurricane Michael hitting Northwest Florida in October 2018.Peer ReviewedPostprint (author's final draft