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Bruck, Miguel, Lehner, Susanne
Journal of Applied Remote Sensing, 01 January 2013, Vol.7(1), pp.073694-073694
[Revue évaluée par les pairs]
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Titre: A Combination of Traditional and Polarimetric Features for Oil Spill Detection Using TerraSAR-X Auteur:Singha, Suman; Ressel, Rudolf; Velotto, Domenico; Lehner, Susanne Sujet:Feature Extraction ; Scattering ; Sea Surface ; Synthetic Aperture Radar ; Entropy ; Surface Contamination ; Eigenvalues and Eigenfunctions ; Feature Extraction ; Feature Ranking ; Near Real Time (Nrt) Services ; Polarimetric Oil Spill Detection ; Support Vector Machine ; Geology Description:
Synthetic aperture radar (SAR) images are operationally used for the detection of oil spills in the marine environment, as they are independent of sun light and weather-induced phenomena. Exploitation of radar polarimetric features for operational oil spill detection is relatively new and until recently those properties have not been extensively exploited. This paper describes the development of a oil spill detection processing chain using coherent dual-polarimetric (copolarized channels, i.e., HH-VV) TerraSAR-X images. The proposed methodology focuses on offshore platform monitoring and introduces for the first time a combination of traditional and polarimetric features for object-based oil spill detection and look-alike discrimination. A total number of 35 feature parameters were extracted from 225 oil spills and 26 look-alikes and divided into training and validation dataset. Mutual information content among extracted features have been assessed and feature parameters are ranked according to their ability to discriminate between oil spill and look-alike. Extracted features are used for training and validation of a support vector machine-based classifier. Performance estimation was carried out for the proposed methodology on a large dataset with overall classification accuracy of 90% oil spills and 80% for look-alikes. Polarimetric features such as geometric intensity, copolarization power ratio, span proved to be more discriminative than other polarimetric and traditional features.
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, November 2016, Vol.9(11), pp.4979-4990
1939-1404 (ISSN); 2151-1535 (E-ISSN); 10.1109/JSTARS.2016.2559946 (DOI)
Titre: Storm observations by remote sensing and influences of gustiness on ocean waves and on generation of rogue waves Auteur:Pleskachevsky, Andrey; Lehner, Susanne; Rosenthal, Wolfgang Sujet:Remote sensing ; Organized wind gusts ; Open atmospheric cell ; Rogue waves ; Wave modeling Description:
The impact of the gustiness on surface waves under storm conditions is investigated with focus on the appearance of wave groups with extreme high amplitude and wavelength in the North Sea. During many storms characterized by extremely high individual waves measured near the German coast, especially in cold air outbreaks, the moving atmospheric open cells are observed by optical and radar satellites. According to measurements, the footprint of the cell produces a local increase in the wind field at sea surface, moving as a consistent system with a propagation speed near to swell wave-traveling speed. The optical and microwave satellite data are used to connect mesoscale atmospheric turbulences and the extreme waves measured. The parameters of open cells observed are used for numerical spectral wave modeling. The North Sea with horizontal resolution of 2.5 km and with focus on the German Bight was simulated. The wind field “storm in storm,” including moving organized mesoscale eddies with increased wind speed, was generated. To take into account the rapid moving gust structure, the input wind field was updated each 5 min. The test cases idealized with one, two, and four open individual cells and, respectively, with groups of open cells, with and without preexisting sea state, as well the real storm conditions, are simulated. The model results confirm that an individual-moving open cell can cause the local significant wave height increase in order of meters within the cell area and especially in a narrow area of 1–2 km at the footprint center of a cell (the cell's diameter is 40–90 km). In a case of a traveling individual open cell with 15 m·s −1 over a sea surface with a preexisting wind sea of and swell, a local significant wave height increase of 3.5 m is produced. A group of cells for a real storm condition produces a local increase of significant wave height of more than 6 m during a short time window of 10–20 min (cell passing). The sea surface simulation from modeled wave spectra points out the appearance of wave groups including extreme individual waves with a period of about 25 s and a wavelength of more than 350 m under the cell's footprint. This corresponds well with measurement of a rogue wave group with length of about 400 m and a period of near 25 s. This has been registered at FiNO-1 research platform in the North Sea during Britta storm on November 1, 2006 at 04:00 UTC. The results can explain the appearance of rogue waves in the German Bight and can be used for ship safety and coastal protection. Presently, the considered mesoscale gustiness cannot be incorporated in present operational wave forecasting systems, since it needs an update of the wind field at spatial and temporal scales, which is still not available for such applications. However, the scenario simulations for cell structures with appropriate travel speed, observed by optical and radar satellites, can be done and applied for warning messages.
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Ocean Dynamics, 2012, Vol.62(9), pp.1335-1351
1616-7341 (ISSN); 1616-7228 (E-ISSN); 10.1007/s10236-012-0567-z (DOI)
Titre: Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images Auteur:Wiehle, Stefan; Lehner, Susanne Contributeur:Marino, Armando Description:
We present an algorithm for automatic detection of the land-water-line from TerraSAR-X images acquired over the Wadden Sea. In this coastal region of the southeastern North Sea, a strip of up to 20 km of seabed falls dry during low tide, revealing mudflats and tidal creeks. The tidal currents transport sediments and can change the coastal shape with erosion rates of several meters per month. This rate can be strongly increased by storm surges which also cause flooding of usually dry areas. Due to the high number of ships traveling through the Wadden Sea to the largest ports of Germany, frequent monitoring of the bathymetry is also an important task for maritime security. For such an extended area and the required short intervals of a few months, only remote sensing methods can perform this task efficiently. Automating the waterline detection in weather-independent radar images provides a fast and reliable way to spot changes in the coastal topography. The presented algorithm first performs smoothing, brightness thresholding, and edge detection. In the second step, edge drawing and flood filling are iteratively performed to determine optimal thresholds for the edge drawing. In the last step, small misdetections are removed.
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Journal of Sensors, 2015, Vol.2015, 6 pages
1687-725X (ISSN); 1687-7268 (E-ISSN); 10.1155/2015/450857 (DOI)
Titre: A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images Auteur:Ressel, Rudolf; Frost, Anja; Lehner, Susanne Sujet:Ice ; Synthetic Aperture Radar ; Feature Extraction ; Time Series Analysis ; Training ; Neural Networks ; Accuracy ; Earth and Atmospheric Sciences ; Pattern Analysis ; Remote Sensing ; Texture ; Geology Description:
We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, July 2015, Vol.8(7), pp.3672-3680
1939-1404 (ISSN); 2151-1535 (E-ISSN); 10.1109/JSTARS.2015.2436993 (DOI)
Titre: First Comparison of Sentinel-1 and TerraSAR-X Data in the Framework of Maritime Targets Detection: South Italy Case Auteur:Velotto, Domenico; Bentes, Carlos; Tings, Bjorn; Lehner, Susanne Sujet:Satellites ; Synthetic Aperture Radar ; Europe ; Marine Vehicles ; Radar Remote Sensing ; Surveillance ; Object Detection ; Radar Tracking ; Multifrequency ; Multipolarization ; Synthetic Aperture Radar (SAR) ; Targets Detection ; Engineering ; Oceanography Description:
The Sentinel-1A is the first of two satellites that composes the Sentinel-1 radar mission. Both satellites operate a C-band synthetic aperture radar (SAR) system to give continuity to the European SAR program. SAR is a flexible sensor able to fulfil users/applications requirements in terms of resolution and coverage thanks to different operational modes and polarizations. With the in-orbit availability of very-high-resolution X-band SAR sensors, the Sentinel-1 satellites have been designed to achieve wide coverage at medium to high resolution. The interferometric wide swath (IWS) mode implemented with the terrain observation with progressive scan (TOPS) technique is the standard acquisition mode over European waters and land masses. IWS in dual-polarization (VV/VH) combination offers 250-km swath at 5 m × 20 m (range × azimuth) spatial resolution. These specifications are in line with the needs of the European Maritime and Security Agency (EMSA) for oil spill and ship detection applications included in the CleanSeaNet program. The main goals of this paper are: assessment of medium-to-high-resolution C-band Sentinel-1 data with very-high-resolution X-band TerraSAR-X data for maritime targets detection; synergetic use of multiplatforms satellite SAR data for target features extraction; evaluation of polarimetric target detectors for the available co-polarization and cross-polarization Sentinel-1A IWS VV/VH products. The objectives are achieved by means of real, almost coincident C-band and X-band SAR data acquired by Sentinel-1A and TerraSAR-X satellites over Gulf of Naples and Catania (South Italy). Furthermore, the obtained results are supported by recorded ground truth vessel reports via terrestrial automatic identification system (AIS) stations located in the area.
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IEEE Journal of Oceanic Engineering, October 2016, Vol.41(4), pp.993-1006
0364-9059 (ISSN); 1558-1691 (E-ISSN); 10.1109/JOE.2016.2520216 (DOI)
Titre: Synergy and fusion of optical and synthetic aperture radar satellite data for underwater topography estimation in coastal areas Auteur:Pleskachevsky, Andrey; Lehner, Susanne; Heege, Thomas; Mott, Claudius Sujet:SAR ; TerraSAR-X ; QuickBird optical images ; Underwater topography Description:
A method to obtain underwater topography for coastal areas using state-of-the-art remote sensing data and techniques worldwide is presented. The data from the new Synthetic Aperture Radar (SAR) satellite TerraSAR-X with high resolution up to 1 m are used to render the ocean waves. As bathymetry is reflected by long swell wave refraction governed by underwater structures in shallow areas, it can be derived using the dispersion relation from observed swell properties. To complete the bathymetric maps, optical satellite data of the QuickBird satellite are fused to map extreme shallow waters, e.g., in near-coast areas. The algorithms for bathymetry estimation from optical and SAR data are combined and integrated in order to cover different depth domains. Both techniques make use of different physical phenomena and mathematical treatment. The optical methods based on sunlight reflection analysis provide depths in shallow water up to 20 m in preferably calm weather conditions. The depth estimation from SAR is based on the observation of long waves and covers the areas between about 70- and 10-m water depths depending on sea state and acquisition quality. The depths in the range of 20 m up to 10 m represent the domain where the synergy of data from both sources arises. Thus, the results derived from SAR and optical sensors complement each other. In this study, a bathymetry map near Rottnest Island, Australia, is derived. QuickBird satellite optical data and radar data from TerraSAR-X have been used. The depths estimated are aligned on two different grids. The first one is a uniform rectangular mesh with a horizontal resolution of 150 m, which corresponds to an average swell wavelength observed in the 10 × 10-km SAR image acquired. The second mesh has a resolution of 150 m for depths up to 20 m (deeper domain covered by SAR-based technique) and 2.4 m resolution for the shallow domain imaged by an optical sensor. This new technique provides a platform for mapping of coastal bathymetry over a broad area on a scale that is relevant to marine planners, managers, and offshore industry.
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Ocean Dynamics, 2011, Vol.61(12), pp.2099-2120
1616-7341 (ISSN); 1616-7228 (E-ISSN); 10.1007/s10236-011-0460-1 (DOI)
Titre: Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar Auteur:Ressel, Rudolf; Suman Singha; Lehner, Susanne; Rosel, Anja; Spreen, Gunnar Sujet:Sea Ice ; Synthetic Aperture Radar ; Neural Networks ; Sea Measurements ; Marine Vehicles ; Ice Thickness ; Artificial Neural Network (Ann) ; Feature Evaluation ; Polarimetry ; Sea Ice Classification ; Terrasar-X ; Geology Description:
Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH-VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, July 2016, Vol.9(7), pp.3131-3143
1939-1404 (ISSN); 2151-1535 (E-ISSN); 10.1109/JSTARS.2016.2539501 (DOI)
Titre: The Potential of TerraSAR-X to Observe Wind Wave Interaction at the Ice Edge Auteur:Gebhardt, Claus; Bidlot, Jean-Raymond; Jacobsen, Sven; Lehner, Susanne; Persson, P. Ola G; Pleskachevsky, Andrey L Sujet:Ice ; Sea State ; Satellites ; Synthetic Aperture Radar ; Wind Forecasting ; Imaging ; Arctic Marginal Seas ; Marginal Ice Zone (Miz) ; Sea State ; Terrasar-X ; Wind ; Geology Description:
This paper performs a study on sea state and wind fields at the ice edge boundary by utilizing information from different sources including synthetic aperture radar (SAR) satellite imagery, weather and sea state analyses from the European Centre for Medium-Range Weather Forecasts, shipborne in-situ measurements, and AMSR2 ice charts. The basis is a Stripmap scene from the TerraSAR-X satellite acquired on October 18, 2015, at ~18 UTC, in support of the cruise of the research vessel R/V Sikuliaq in the Beaufort/Chukchi Sea. This scene covers an area with a length of more than 100 km and comprises both the marginal ice zone and, for the largest part, open water. The wave and wind field is retrieved from satellite at high spatial resolution using empirical retrieval algorithms. These algorithms are XWAVE and XMOD-2 specifically developed for X-Band SAR. XWAVE allows for determining the significant wave height not only for long swell waves, but also for short waves with their wave pattern being hardly visible from SAR. The latter is based on the analysis of image spectrum parameters and spectral noise. As well, the possibility of the imaging quality of longer waves visible from SAR being affected by SAR-specific nonlinear imaging effects is narrowed down. Both the wave and wind field are found to exhibit considerable spatial variability, and their relationship is analyzed. The relevance of the findings of this study with respect to wave/ice modeling is discussed.
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, June 2017, Vol.10(6), pp.2799-2809
1939-1404 (ISSN); 2151-1535 (E-ISSN); 10.1109/JSTARS.2017.2652124 (DOI)