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| Citation: | AI Jiaqiu, CAO Zhenxiang, MAO Yuxiang, et al. An improved bilateral CFAR ship detection algorithm for SAR image in complex environment[J]. Journal of Radars, 2021, 10(4): 499–515. doi: 10.12000/JR20127
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An Improved Bilateral CFAR Ship Detection Algorithm for SAR Image in Complex Environment
DOI: 10.12000/JR20127
More Information-
Abstract
The Bilateral Constant False Alarm Rate (BCFAR) detection algorithm calculates the spatial information of Synthetic Aperture Radar (SAR) image by the Gaussian kernel density estimator, and combines it with the intensity information of image to obtain the joint image for target detection. Compared with the classical CFAR detection algorithm which uses only intensity information for target detection, bilateral CFAR has better detection performance and robustness. However, with continuous high-intensity heterogeneous points (such as breakwater, azimuth ambiguity and phantom) in a complex environment, spatial information calculated by kernel density estimator will have more errors, which will lead to many false alarms in detection results. In addition, when it comes to a weak target with less similarity between adjacent pixels, it will miss detection. To effectively improve these problems, this paper designs an Improved Bilateral CFAR (IB-CFAR) algorithm in complex environment. The IB-CFAR proposed in this paper is mainly divided into three stages: intensity level division based on the nonuniform quantization method, intensity spatial domain information fusion and parameter estimation after clutter truncation. The intensity level division based on the nonuniform quantization method can improve the similarity and contrast information of weak targets, leading to improved ship detection rate. The information fusion of strength spatial domain is to fuse the spatial similarity, distance direction and strength information, which can further improve the detection rate and describe the ship structure information. Parameter estimation after clutter truncation can remove continuous high-intensity heterogeneous points in the background window and retain the real sea clutter samples to the maximum extent, which makes parameter estimation more accurate. Finally, according to the estimated parameters, an accurate sea clutter statistical model is established for CFAR detection. In this paper, the effectiveness and robustness of the proposed algorithm are verified by using GaoFen-3 and TerraSAR-X data.The experimental results show that the proposed algorithm performs well in the environment with more dense distribution of weak targets, and can obtain 97.85% detection rate and 3.52% false alarm rate in such environment. Compared with the existing detection algorithms, the detection rate increased by 5% and the false alarm rate reduced by 10%. However, when the number of weak targets is small and the background is very complex, few false alarms will appear. -
References
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Zhou Yu, Wang Hai-peng, Chen Si-zhe. SAR Automatic Target Recognition Based on Numerical Scattering Simulation and Model-based Matching[J]. Journal of Radars, 2015, 4(6): 666-673. doi: 10.12000/JR15080
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- Figure 1. The comparison between the value of weak target in the joint image of bilateral CFAR and the intensity value in the original image
- Figure 2. The proposed IB-CFAR detection flow chart
- Figure 3. Schematic diagram of non-uniform strength grading, which yellow area represents the level N
- Figure 4. Variation curves of various performance indicators at different levels
- Figure 5. Non-uniform quantization method enhances similarity information within weak targets
- Figure 6. Clutter suppression effect of bilateral CFAR in the presence of strong speckle noise
- Figure 7. The comparison of the fusion images of bilateral CFAR and IB-CFAR under the background of dense distribution of high-intensity heterogeneous points
- Figure 8. Clutter suppression effect of IB-CFAR in the presence of strong speckle noise
- Figure 9. The high-intensity outliers elimination performance evaluation of OR-CFAR and the proposed IB-CFAR
- Figure 10. Gaofen-3 test image of the homogeneous sea area near Shanghai harbor acquired by UFS mode on February 25, 2017
- Figure 11. High-resolution, multi-look, HH polarized SAR image of the Panama Canal region acquired by the X-band TerraSAR SM mode on July 31, 2009
- Figure 12. Gaofen-3 test image of the heterogeneous open sea of Yangtze River Estuary acquired by the FSI mode on September 1, 2018
- Figure 13. Comparison of detection results in complex environment, which contains 30 targets with dense distribution, and there are ghost and anti wave in the image
- Figure 14. Comparison of original intensity domain image and fusion domain image at the same coordinate point
- Figure 15. Comparison of detection results
- Figure 16. Comparison of detection results in complex environment
- Figure 17. ROC curves of various CFAR detectors