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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
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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. -
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References
[1] 许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9(4): 684–714. doi: 10.12000/JR20084XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084[2] 李春升, 于泽, 陈杰. 高分辨率星载SAR成像与图像质量提升方法综述[J]. 雷达学报, 2019, 8(6): 717–731. doi: 10.12000/JR19085LI Chunsheng, YU Ze, and CHEN Jie. Overview of techniques for improving high-resolution spaceborne SAR imaging and image quality[J]. Journal of Radars, 2019, 8(6): 717–731. doi: 10.12000/JR19085[3] 杜兰, 王兆成, 王燕, 等. 复杂场景下单通道SAR目标检测及鉴别研究进展综述[J]. 雷达学报, 2020, 9(1): 34–54. doi: 10.12000/JR19104DU Lan, WANG Zhaocheng, WANG Yan, et al. Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes[J]. Journal of Radars, 2020, 9(1): 34–54. doi: 10.12000/JR19104[4] GREENSPAN M, PHAM L, and TARDELLA N. Development and evaluation of a real time SAR ATR system[C]. 1998 IEEE Radar Conference, RADARCON’98. Challenges in Radar Systems and Solutions, Dallas, USA, 1998: 38–43.[5] DAI Hui, DU Lan, WANG Yan, et al. A modified CFAR algorithm based on object proposals for ship target detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1925–1929. doi: 10.1109/LGRS.2016.2618604[6] NOVAK L M, OWIRKA G J, and NETISHEN C M. Radar target identification using spatial matched filters[J]. Pattern Recognition, 1994, 27(4): 607–617. doi: 10.1016/0031-3203(94)90040-X[7] RAES R L, LORENZZETTI J A, and GHERARDI D F M. Ship detection using TerraSAR-X images in the Campos basin (Brazil)[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 545–548. doi: 10.1109/LGRS.2010.2041322[8] WANG Zhaocheng, DU Lan, ZHANG Peng, et al. Visual attention-based target detection and discrimination for high-resolution SAR images in complex scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 1855–1872. doi: 10.1109/TGRS.2017.2769045[9] GINI F and RANGASWAMY M. Knowledge-Based Radar Detection, Tracking, and Classification[M]. Hoboken, USA: Wiley-Interscience, 2008: 167–196.[10] CRISP D J. The state-of-the-art in ship detection in synthetic aperture radar imagery[R]. DSTO-RR-0272, 2004: 2–30.[11] AI Jiaqiu, CAO Zhenxiang, and XING Mengdao. An adaptive-trimming-depth based CFAR detector of heterogeneous environment in SAR imagery[J]. Remote Sensing Letters, 2020, 11(8): 730–738. doi: 10.1080/2150704X.2020.1763501[12] QIN Xianxiang, ZHOU Shilin, ZOU Huanxin, et al. A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 806–810. doi: 10.1109/LGRS.2012.2224317[13] OLIVER C J. A model for non-Rayleigh scattering statistics[J]. Optica Acta: International Journal of Optics, 1984, 31(6): 701–722. doi: 10.1080/713821561[14] LIAO Mingsheng, WANG Changcheng, WANG Yong, et al. Using SAR images to detect ships from sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(2): 194–198. doi: 10.1109/LGRS.2008.915593[15] GAO Gui, LIU Li, ZHAO Lingjun, et al. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1685–1697. doi: 10.1109/TGRS.2008.2006504[16] FARROUKI A and BARKAT M. Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments[J]. IEE Proceedings - Radar, Sonar and Navigation, 2005, 152(1): 43–51. doi: 10.1049/ip-rsn:20045006[17] JIANG Wen, HUANG Yulin, and YANG Jianyu. Automatic censoring CFAR detector based on ordered data difference for low-flying helicopter safety[J]. Sensors, 2016, 16(7): 1055. doi: 10.3390/s16071055[18] BLAKE S. OS-CFAR theory for multiple targets and nonuniform clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(6): 785–790. doi: 10.1109/7.18645[19] AI Jiaqiu, TIAN Ruitian, LUO Qiwu, et al. Multi-scale rotation-invariant haar-like feature integrated CNN-based ship detection algorithm of multiple-target environment in SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 10070–10087. doi: 10.1109/TGRS.2019.2931308[20] AI Jiaqiu, YANG Xuezhi, SONG Jitao, et al. An adaptively truncated clutter-statistics-based two-parameter CFAR detector in SAR imagery[J]. IEEE Journal of Oceanic Engineering, 2018, 43(1): 267–279. doi: 10.1109/JOE.2017.2768198[21] AI Jiaqiu, LUO Qiwu, YANG Xuezhi, et al. Outliers-robust CFAR detector of Gaussian clutter based on the truncated-maximum-likelihood- estimator in SAR imagery[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 2039–2049. doi: 10.1109/TITS.2019.2911692[22] BRUSCH S, LEHNER S, FRITZ T, et al. Ship surveillance with TerraSAR-X[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3): 1092–1103. doi: 10.1109/TGRS.2010.2071879[23] WANG Chao, JIANG Shaofeng, ZHANG Hong, et al. Ship detection for high-resolution SAR images based on feature analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 119–123. doi: 10.1109/LGRS.2013.2248118[24] LENG Xiangguang, JI Kefeng, YANG Kai, et al. A bilateral CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1536–1540. doi: 10.1109/LGRS.2015.2412174[25] RAYNAL A M and DOERRY A W. Doppler characteristics of sea clutter[R]. SAND2010-3828, 2010.[26] 陈小龙, 关键, 黄勇, 等. 雷达低可观测目标探测技术[J]. 科技导报, 2017, 35(11): 30–38. doi: 10.3981/j.issn.1000-7857.2017.11.004CHEN Xiaolong, GUAN Jian, HUANG Yong, et al. Radar low-observable target detection[J]. Science &Technology Review, 2017, 35(11): 30–38. doi: 10.3981/j.issn.1000-7857.2017.11.004[27] LIU Y, FRASIER S J, and MCINTOSH R E. Measurement and classification of low-grazing-angle radar sea spikes[J]. IEEE Transactions on Antennas and Propagation, 1998, 46(1): 27–40. doi: 10.1109/8.655448[28] WANG Chonglei, BI Fukun, ZHANG Weiping, et al. An intensity-space domain CFAR method for ship detection in HR SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(4): 529–533. doi: 10.1109/LGRS.2017.2654450 -
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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