Reflection Symmetry for Polarimetric Observation of Man-Made Metallic Targets at Sea

Nunziata, Ferdinando; Migliaccio, M.; Brown, Carl E.

doi:10.1109/joe.2012.2198931

Cited by 140 publications

(78 citation statements)

References 24 publications

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“…In addition, besides the volume scatterers, the oriented scatterers on ships such as oriented dihedral corners and wires can also generate HV scattering; hence, the HV scattering power corresponding to volume scatterers on ships should be low. (2) Reflection symmetry is a powerful tool by which the essential difference between ship targets and sea clutter can be characterized [27]. The natural sea background scenario possesses symmetry properties, while such properties no longer apply with man-made metallic targets, i.e., ships at sea.…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…• IC4G: The original C4G has nine equations (refer to Equation (27) in [24]), but ten unknown parameters if the imaginary part of β is considered. To make the nonlinear least squares optimization problem have a determined solution, Chen et al neglected the imaginary part of β considering that β can be approximated as a real value for most natural surfaces.…”

Section: Sar Data and Comparative Methodsmentioning

confidence: 99%

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Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Lang

Tao

et al. 2017

Remote Sensing

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Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel cross-polarized scattering component capable of discriminating between the HV scattering power generated by the oriented scatterers on ships from that by volume scatterers is proposed as a means to address the problem of volume scattering power overestimation. In the decomposition stage, by taking into account both the real and imaginary parts of the elements [T] HV (1, 3) and [T] HV (2, 3) of the observed coherency matrix, the proposed cross-polarized component can preserve the reflection asymmetry information completely, which is an essential property of man-made targets, such as ships. Based on the proposed decomposition method and an analysis of the different SMs between ships and sea clutter, a novel ship detection metric defined as M = lnis proposed. Experimental results conducted on RadarSat-2 quad-polarimetric data validate the proposed four-component decomposition method as being more suitable for analyzing the SMs of ship targets than the existing helix matrix-based decomposition methods. Additionally, we find that the proposed ship detection metric can effectively enhance the signal-to-clutter ratio (SCR) and improve ship detection performance.

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Section: Decomposition Methodsmentioning

confidence: 99%

Section: Sar Data and Comparative Methodsmentioning

confidence: 99%

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Lang

Tao

et al. 2017

Remote Sensing

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“…Specifically, the cross polarization channel and the ratio between cross-and co-polarizations (here referred to as depolarization ratio) increases. Ships are composed by a large collection of structures (mostly metallic) [4,5]. The orientation of these structures is generally random and therefore they are expected to have a large cross-polarisation contribution [6].…”

Section: Dimensionless Detectormentioning

confidence: 99%

Ship detection with Cosmo-SkyMed PINGPONG data using the dual-pol ratio anomaly detector

Marino

Iervolino

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Extensive work has been carried out on detecting ships using space-borne Synthetic Aperture Radar (SAR) systems. However, the identification of small vessels is still challenging especially when the sea conditions are rough. In this work, a new detector is proposed based on dual-polarized incoherent SAR images. Small ships have a stronger cross polarization accompanied by a higher cross-over co-polarization ratio compared to sea. This is the rational at the base of the detector.The new detector is tested with dual-polarization HH/HV PINGPONG Cosmo-SkyMed images acquired over the North Sea. The test area is near Rotterdam where a large number of ships are expected.Index Terms-SAR, polarimetry, ship detection, COSMOSkyMed SAR POLARIMETRYBased on its polarization characteristics, a single target can be characterized using the scattering (Sinclair) matrix or equivalently a scattering vector k [1]. In the lexicographic basis system and in case of reciprocity and monostatic system, the scattering vector is k L = [HH, √ 2HV, V V ], where H stands for linear horizontal and V for linear vertical (therefore the HV image is obtained transmitting a linear vertical polarization and receiving the linear horizontal one). HH and V V are often referred to as co-channels and HV or V H are the cross-channels.

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“…Alternative approaches to the detection include wavelet analysis [13], use of a neural network [14,15], segmentation [16], non-parametric models [17] or saliency [18]. For use on complex SAR images, which SUMO does not exploit, yet other techniques are possible, such as split-look or sub-apertures [19], coherent-to-incoherent ratio [20] and polarimetric methods [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

The SUMO Ship Detector Algorithm for Satellite Radar Images

et al. 2017

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Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C-and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1-100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU's Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.

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Reflection Symmetry for Polarimetric Observation of Man-Made Metallic Targets at Sea

Cited by 140 publications

References 24 publications

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

Ship detection with Cosmo-SkyMed PINGPONG data using the dual-pol ratio anomaly detector

The SUMO Ship Detector Algorithm for Satellite Radar Images

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