A Bilateral CFAR Algorithm for Ship Detection in SAR Images

Leng, Xiangguang; Ji, Kefeng; Yang, Kai; Zou, Huanxin

doi:10.1109/lgrs.2015.2412174

Cited by 192 publications

(35 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Leng et al proposed a bilateral CFAR method that considers both pixel intensity and spatial distribution. Compared with ordinary CFAR, it is able to reduce the impact of sea clutter and SAR image ambiguity [6]. Schwegmann et al proposed to convert the thresholds of cell average CFAR into diversified thresholds for different backgrounds, and conducted simulated annealing to select the appropriate threshold value for target detection combined with ship distribution [7].…”

Section: Introductionmentioning

confidence: 99%

A Small Ship Target Detection Method Based on Polarimetric SAR

2019

View full text Add to dashboard Cite

The detection of small fishing ships is very important for maritime fishery supervision. However, it is difficult to detect small ships using synthetic aperture radar (SAR), due to the weak target scattering and very small number of pixels. Polarimetric synthetic aperture radar (PolSAR) has been widely used in maritime ship detection due to its abundant target scattering information. In the present paper, a new ship detector, named ΛM, is developed based on the analysis of polarization scattering differences between ship and sea, then combined with the two-parameter constant false alarm rate method (TP-CFAR) algorithm to conduct ship detection. The goals of the detector construction are to fully consider the ship’s depolarization effect, and further amplify it through sliding window processing. First, the signal-to-clutter ratio (SCR) enhancement performance of ΛM for ships with different lengths ranging from 8 to 230 m under 90 different combinations of windows are analyzed in detail using three set of RADARSAT-2 quad-polarization data, then the appropriate window size is determined. In addition, the SCR enlargement between ΛM and some typical polarization features is compared. Among these, for ships of length greater than 35 m, the average contrast of ΛM is 33.7 dB, which is 20 dB greater than that of the HV channel. For small vessels of length less than 16 m, the average contrast of ΛM is 16 dB higher than that of HV channel on average. Finally, the RADARSAT-2 data including nonmetallic small vessels are used to perform ship detection tests, and the detection ability for conventional and small ships of some classic algorithms are compared and analyzed. For large vessels of length greater than 35 m, the method proposed in this paper is able to obtain a superior detection result, maintain the ship contour well, and suppress false alarms caused by the cross side lobe in the SAR image. For small vessels of length less than 16 m, the method proposed in this paper can reduce the number of missed targets, while also obtaining superior detection results, especially for small nonmetallic vessels.

show abstract

Section: Introductionmentioning

confidence: 99%

A Small Ship Target Detection Method Based on Polarimetric SAR

2019

View full text Add to dashboard Cite

show abstract

“…The constant false alarm rate (CFAR) algorithm is a commonly used method for computing the threshold to be applied to distinguish the backscattered signal of the targets from the background clutter of the sea surface [7][8][9]. The principle of the CFAR algorithm is to calculate the CFAR by adapting the statistical properties of the observed SAR images to those from a specific statistical model of the sea surface [10,11].…”

Section: Introductionmentioning

confidence: 99%

The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

et al. 2018

View full text Add to dashboard Cite

Modeling the statistical distribution of synthetic aperture radar (SAR) images is essential for sea target detection, which is an important aspect of marine SAR applications. The main goal of this study is to determine the effects of sea states and surface wave texture characteristics on the statistical distributions of sea SAR images. A statistical analysis of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode images (imagettes), covering a variety of sea states and wave conditions, was carried out to investigate the suitability of the statistical distributions often used in the literature for sea states parameters. The results revealed the variation in the distribution parameters in terms of their azimuthal cutoff wavelength (ACW) and the peak-to-background ratio (PBR) of the SAR image intensity spectra. The shape parameters of Gamma and Weibull distribution are sensitive and monotonously decreasing with respect to PBR, while the scale parameter is sensitive to ACW. The K distribution was shown to perform well, with both high and stable accuracy. The results of this paper provide a parameterized scheme for sea state classifications and can potentially be used for choosing the most suitable distribution model according to sea state when performing sea target detection.

show abstract

“…Since decades of research for ship detection, scads of algorithms have been studied with rather good results. The most frequently used ship detection algorithm is the intensity-based constant false alarm rate (CFAR) method with a suitable probability density function (PDF) for the background clutter model [6,7,8]. Other methods are based on the polarimetric analysis [9,10,11] and multi-look cross-correlation [12,13], etc.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Zhao

Liu

2018

Sensors

View full text Add to dashboard Cite

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (GΓD) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

show abstract

A Bilateral CFAR Algorithm for Ship Detection in SAR Images

Cited by 192 publications

References 9 publications

A Small Ship Target Detection Method Based on Polarimetric SAR

A Small Ship Target Detection Method Based on Polarimetric SAR

The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Contact Info

Product

Resources

About