CFAR Ship Detection in Nonhomogeneous Sea Clutter Using Polarimetric SAR Data Based on the Notch Filter

Gao, Gui; Shi, Gongtao

doi:10.1109/tgrs.2017.2701813

Cited by 88 publications

(62 citation statements)

References 33 publications

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“…Recently, a Polarimetric Notch Filter (PNF) was proposed that tries to separate ships and vessels based on their polarimetric behavior [11]- [12]. It does not assume a prior information about the ships and can provide good performance by minimizing the sea clutter power [1][13]- [14]. Another filter working in the absence of ship prior information is the Polarimetric Whitening Filter (PWF) proposed by Novak [15] [16].…”

Section: A Review Of Ship Detection In Polsar Imagerymentioning

confidence: 99%

“…The constant false alarm rate (CFAR) tries to keep PFA constant by selecting a suitable threshold that adapts with the background clutter [1]. To adapt the threshold an accurate modeling and estimation of the statistical distribution of local background clutter is generally done using a sliding window [1][13]- [14]. Different statistical models are used to fit heterogeneous sea clutter [22].…”

Section: A Review Of Ship Detection In Polsar Imagerymentioning

confidence: 99%

“…This does not assure us the absence of ships in the training region. However, if ships are few and the region large, the effect of contamination on the statistical model can be ignored as seen in Gao et al [14]. This procedure requires supervision.…”

Section: B Survey Of Cfar Detectors In the Contaminated Surroundingsmentioning

confidence: 99%

“…One part of the SAR pixel variation is due to the interference between scatterers within a resolution cell, which is also referred to as speckle. The other part of the variation is due to the fluctuation of the radar cross section (RCS), which is also referred to as texture [14]. The variation generated by texture is slower than the one generated by speckle.…”

Section: A Description Of Pol-sar Datamentioning

confidence: 99%

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Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Liu

Yang

Marino

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Constant false alarm rate (CFAR) algorithms using local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this paper a polarimetric truncated statistic CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for Pol SAR imagery. The CFAR detector uses polarimetric whitening filter (PWF) to turn the multidimensional problem to a one dimensional case. It uses the truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require a prior knowledge of the interfering targets and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations and the results are in line with the theory.

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Section: A Review Of Ship Detection In Polsar Imagerymentioning

confidence: 99%

Section: A Review Of Ship Detection In Polsar Imagerymentioning

confidence: 99%

Section: B Survey Of Cfar Detectors In the Contaminated Surroundingsmentioning

confidence: 99%

Section: A Description Of Pol-sar Datamentioning

confidence: 99%

See 2 more Smart Citations

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Liu

Yang

Marino

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…An important application for marine surveillance radar is to detect sea-surface small floating targets such as buoys, human divers, and small boats [1]. When detecting, the received target signals at the radar are buried in the strong returned signals reflected by the sea surface, referred to as sea clutters [2], [3]. It is known that better detection performance is achieved if prior knowledge of sea clutters' distribution can be acquired, since by this a proper detection threshold can be determined at the detector [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach

Zhou

Jiang

2018

2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)

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An accurate sea clutter distribution is crucial for decision region determination when detecting sea-surface floating targets. However, traditional parametric models possibly have a considerable gap to the realistic distribution of sea clutters due to the volatile sea states. In this paper, we develop a kernel density estimation based framework to model the sea clutter distributions without requiring any prior knowledge. In this framework, we jointly consider two embedded fundamental problems, the selection of a proper kernel density function and the determination of its corresponding optimal bandwidth. Regarding these two problems, we adopt the Gaussian, Gamma, and Weibull distributions as the kernel functions, and derive the closed-form optimal bandwidth equations for them. To deal with the highly complicated equations for the three kernels, we further design a fast iterative bandwidth selection algorithm to solve them. Experimental results show that, compared with existing methods, our proposed approach can significantly decrease the error incurred by sea clutter modeling (about two orders of magnitude reduction) and improve the target detection probability (up to 36% in low false alarm rate cases).

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Ship Detection in SAR Images Based on Region Growing and Multi-scale Saliency

Liu

2020

Pattern Recognition and Computer Vision

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CFAR Ship Detection in Nonhomogeneous Sea Clutter Using Polarimetric SAR Data Based on the Notch Filter

Cited by 88 publications

References 33 publications

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Sea Clutter Distribution Modeling: A Kernel Density Estimation Approach

Ship Detection in SAR Images Based on Region Growing and Multi-scale Saliency

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