A Comprehensive Facet Model for Bistatic Sar Imagery of Dynamic Ocean Scene

Zhao, Yan; Zhang, Min; Geng, Xupu; Ping, Zhou

doi:10.2528/pier11100910

Cited by 11 publications

(12 citation statements)

References 34 publications

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“…The geometric sketch is illustrated in Figure 6. As for the assumption of neglecting speckle, system noise and the range-azimuth coupling, the ensemble averaged SAR image intensity distribution I(x, y) is given by [29][30][31] I (x, y) = where, u r (x g , y g ) means the radial component of the mean orbital velocity of a wave patch. σ I (x g , y g ) is the mean NRCS of surface wave modified by tilt modulation and hydrodynamic modulation by the relationship…”

Section: Sar Image Simulation Of Kelvin Wave Wakementioning

confidence: 99%

Application of Facet Scattering Model in Sar Imaging of Sea Surface Waves With Kelvin Wake

Wang¹,

Min²,

Chen³

et al. 2016

PIER B

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Abstract-The principal purpose of this work is the simulation of the ship wake in Synthetic Aperture Radar (SAR) imaging based on a facet scattering model. The hydrodynamic model of the surface waves mainly considers the Kelvin wake waves and the wind driven waves. For the prediction of radar returns from the composite surface, the semi-deterministic facet scattering model (SDFSM) is proposed, which is verified to have good performance through a comparison with the experiment by SASS-II. Then, the distributions of backscattering normalized radar cross section (NRCS) of facets are investigated for both V V and HH polarizations and characteristics of the wake pattern are shown with good visibility. On the basis of these, an application of velocity bunching (VB) imaging model is presented in detail for the simulation of SAR imaging of sea surface waves with Kelvin wake. Finally, several numerical results provide states of the effects of ship speed, wind speed and the ship sailing direction on the characteristics of Kelvin wake in SAR images. Thus, this simulation may enable us to provide a theoretical basis to the detection of ship wakes.

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Section: Sar Image Simulation Of Kelvin Wave Wakementioning

confidence: 99%

Application of Facet Scattering Model in Sar Imaging of Sea Surface Waves With Kelvin Wake

Wang¹,

Min²,

Chen³

et al. 2016

PIER B

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“…Research on the electromagnetic (EM) scattering from random rough surfaces is widely employed in the fields of globe remote sensing, sea research, surface detection and radar imaging [1][2][3]. In actual application, the data of EM scattering are mainly from practical measurement and theoretical deduction.…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic Scattering Analysis for Two-Dimensional Gaussian Rough Surfaces With Texture Characteristics Using Small-Slope Approximation Method

Sun¹,

Xie²,

Zhang³

2015

PIER B

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Abstract-This paper is aimed at analyzing the electromagnetic (EM) scattering from the twodimensional (2-D) Gaussian rough surfaces characterized by textures. Visual appearances of the stripe texture can be generated through the angle rotating in Fourier transform when the ratio of the correlation lengths in two directions is large enough. The scattering field is derived in Cartesian coordinate system through the small-slope approximation (SSA) method with plane incident wave. The normalized co-polarized radar cross section (NRCS) from 2-D Gaussian rough surface characterized by textures is calculated. In particular, several numerical results show the influences of incident angle, texture angle, correlation length, and root-mean-square height on the scattering from the textured rough surface. Finally, the validity of the SSA method is verified by comparisons of theoretical value and measured data.

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“…An excellent statistical clutter model should provide a good fitting to SAR image clutter in many practical scenarios. Unfortunately, less attention has been devoted to land scattering in high-resolution SAR, while many investigations have been carried out in the statistical analysis and modeling of high-resolution ocean SAR images [23][24][25][26]. Actually, as the same as the sea clutter, the statistics of the SAR image clutter of the ground areas depends on many complicated factors, such as the radar's operating mode, grazing angle and background operating environment, etc.. As a result, it is very hard to found a universal model for all kinds of ground SAR clutter.…”

Section: Introductionmentioning

confidence: 99%

Cfar Target Detection in Ground Sar Image Based on Kk Distribution

Gao¹,

Zhan²,

Wan³

et al. 2013

PIER

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Abstract-This paper deals with the problem of constant false alarm rate (CFAR) target detection in high-resolution ground synthetic aperture radar (SAR) images based on KK distribution.For the parameter estimation of KK distribution, the semi-experiential algorithm is analyzed firstly. Then a new estimation algorithm based on the particle swarm optimization (PSO) is proposed, which takes the discrepancies between the histogram of the clutter data and probability density function (PDF) of KK distribution at some selected points as the cost function to search for the optimal parameter values using PSO algorithm. The performance of the two algorithms is compared using Monte-Carlo simulation using the simulated data sets generated under different conditions; and the estimation results validate the better performance of the new algorithm. Then the KK distribution, which is proposed for spiky sea clutter originally, is applied to model the real ground SAR clutter data. The goodness-of-fit test clearly show that the KK distribution is able to model the ground SAR clutter much better than some common used model, such as standard Kdistribution and Gamma, etc. On this basis, a global CFAR target detection algorithm is presented. The detection threshold is calculated numerically through the cumulative density function (CDF) of KK distribution. Comparing the amplitude of every SAR image pixel with this threshold, the potential targets in ground SAR images can be located effectively. Then target clustering is implemented to eliminate the false alarm and obtain more accurate target regions. The detection results of the proposed algorithm in a typical ground SAR image show that it has better performance than the detector based on G 0 distribution.

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A Comprehensive Facet Model for Bistatic Sar Imagery of Dynamic Ocean Scene

Cited by 11 publications

References 34 publications

Application of Facet Scattering Model in Sar Imaging of Sea Surface Waves With Kelvin Wake

Application of Facet Scattering Model in Sar Imaging of Sea Surface Waves With Kelvin Wake

Electromagnetic Scattering Analysis for Two-Dimensional Gaussian Rough Surfaces With Texture Characteristics Using Small-Slope Approximation Method

Cfar Target Detection in Ground Sar Image Based on Kk Distribution

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