On the Usage of GRECOSAR, an Orbital Polarimetric SAR Simulator of Complex Targets, to Vessel Classification Studies

Margarit, G.; Mallorquí, J.J.; Rius, J.M.; Sanz-Marcos, J.

doi:10.1109/tgrs.2006.881120

Cited by 92 publications

(66 citation statements)

References 17 publications

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“…Synthetic Aperture Radar (SAR) image databases have an important role in target recognition and identification system development (Lee 1980;Rihaczek and Hershkowitz 2000;Margarit et al 2006;Kasim et al 2008;Lee and Pottier 2009;Guo et al 2010;Huang and Lee 2010;Tian et al 2011;Wang et al 2011). It is mainly dependent on the operator's prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

Sun¹,

Tsai²,

Chiang³

2016

Terr. Atmos. Ocean. Sci.

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Synthetic Aperture Radar (SAR) is a powerful tool for studying natural environments under all-weather and day-andnight conditions. SAR system design and data-processing algorithm simulation is noted for its controllable parameters. The satellite SAR echo signal simulation framework has been successfully applied to target recognition based on Radarsat-2 and TerraSAR-X images and in strip map mode. However, such SAR image simulation works only on CPU or GPU (graphics processing units) and requires huge calculations. We developed a "Load-Balancing Model (LBM)" algorithm that uses Message Passing Interface GPU (MPI-GPU) to reduce the inner loop load and improve the computational performance. The LBM algorithm uses MPI-GPU technology to build the simple GPU cluster system. The LBM algorithm is used to separate the intensive computing and controlling tasks for each node, and exploit the contemporary GPU computation capability to accelerate the computing tasks. We conducted a relevant experiment on a target radar cross section (RCS) and improved the performance by a factor of > 40 compared to a 4-core CPU accelerated program.

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Section: Introductionmentioning

confidence: 99%

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

Sun¹,

Tsai²,

Chiang³

2016

Terr. Atmos. Ocean. Sci.

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“…However, the classification accuracy of these methods is rather limited in low or medium resolution SAR images, without having the means to be further improved. Recently, research has shifted to the exploitation of polarimetric SAR data properties presented by (Touzi, 2004), (Margarit, 2006) and (Margarit, 2009), which utilize the composite mechanisms of ships based on combinations of basic scattering procedures. The geometric properties of the polarimetric scattering behaviour can act as a good estimate of the vessel's category under investigation.…”

Section: Introductionmentioning

confidence: 99%

Vessel Classification in Cosmo-Skymed Sar Data Using Hierarchical Feature Selection

Makedonas

Theoharatos

Tsagaris

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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“…Recently, ocean surveillance with SAR data has been researched widely. The most representative are the Canada's Ocean Monitor Workshop (OMW), the American's Alaska SAR Demo, and the series projects promoted by European Community, such as IMPAST, DECLIMS and LIMES (Margarit, 2006;LIMES, 2006). In this context, new high resolution SAR sensors are very attractive, because they promise to improve the performance of the whole scheme.…”

Section: Introductionmentioning

confidence: 99%

“…A conventional CFAR detector searches ship targets adaptively in the whole imagery with a sliding window, which consumes much time and cannot meet the near real-time processing requirement (Xiang, 2012). In addition, research has recently shifted to the exploitation of polarimetric SAR data properties (Touzi, 2004;Margarit, 2006), which utilize the composite mechanisms of ships based on combinations of basic scattering procedures. The geometric properties of the polarimetric scattering behaviour can act as a good estimate for discriminating ships from sea clutter.…”

Section: Introductionmentioning

confidence: 99%

Detection of Ship Targets in Polarimetric Sar Data Using 2d-Pca Data Fusion

Theoharatos

Makedonas

Fragoulis

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

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On the Usage of GRECOSAR, an Orbital Polarimetric SAR Simulator of Complex Targets, to Vessel Classification Studies

Cited by 92 publications

References 17 publications

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

SAR Image Simulations Using the LBM Algorithm on MPI-GPU

Vessel Classification in Cosmo-Skymed Sar Data Using Hierarchical Feature Selection

Detection of Ship Targets in Polarimetric Sar Data Using 2d-Pca Data Fusion

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