Candidate Region Extraction Method for Multi-satellite and Multi-resolution SAR Ships
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摘要:
基于CFAR和核密度估计(KDE)的SAR传统舰船候选区域提取方法存在以下缺陷：CFAR虚警率依赖人工经验选择；CFAR仅对杂波分布建模，会对被检目标构成一定的漏检风险；利用KDE进行强海杂波过滤时，需凭人工经验选择滤除阈值。这使得传统舰船候选区域提取方法无法适应多星多分辨率等复杂场景。该文提出一种面向多星多分辨率的SAR图像舰船候选区域提取算法，针对CFAR算法的缺陷，提出采用均值二分法迭代逼近目标计算分割阈值，在克服CFAR缺陷的同时，计算效率比CFAR提高10倍以上；针对KDE的缺陷，提出了区块KDE结合大阈值滤除强海杂波，再借助种子点生长算法重建目标。由于大阈值具有足够的阈量，使得算法可以适应更复杂的场景。实验表明所提方法具有不漏检、阈值自适应、计算效率高、虚警率低的优点，具备优秀的多星多分辨率SAR舰船候选区域提取能力。
Abstract:The traditional methods based on CFAR and Kernel Density Estimation (KDE) for SAR ship candidate region extraction has the following defects: The choice of false alarm rate of CFAR depends on artificial experience; CFAR only models the sea clutter distribution, which poses a certain risk of missing detection to the target; When KDE is used to filter strong sea clutter, the threshold must be selected by artificial experience. These defects make the traditional method unable to adapt to complex scene, such as multi-satellite and multi-resolution. A candidate region extraction method for multi-satellite and multi-resolution SAR ships is proposed. In view of the defects of CFAR, an iterative method of mean dichotomy is proposed to approximate the target and calculate the segmentation threshold. The calculation efficiency of this method is more than 10 times higher than that of CFAR while overcoming the defects of CFAR; In view of the defects of KDE, block KDE combined with large threshold is used to filter strong sea clutter, and then seed point growth algorithm is used to reconstruct target. Because the large threshold has enough thresholds, the method can adapt to more complex scenarios. Experiments show that the proposed method has the advantages of no missed detection, self-adaptive threshold, high computational efficiency, and low false alarm rate. It has excellent multi-satellite and multi-resolution SAR ship candidate region extraction capability.
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表 1 典型舰船尺度表
船舶类型 船舶名称 船长(m) 船宽(m) 船舶类型 船舶名称 船长(m) 船宽(m) 集装箱船 COSCO_KAWASAKI 260 32 油船 ZHONG CHI 188 31 N.Y.K.LEO 300 40 WANG CHI 187 32 HYUNDAI_BRIDGE 182 35 驱逐舰 055型 160～180 21～23 HUA_RUN_CHUANG_YE 190 31 日本金刚级 161 21 
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表 2 该实验场景下6种分布CFAR的Pfa的合理取值表
分布名称 Pfa (%) 分布名称 Pfa (%) 高斯分布 2～3 指数分布 10～15 韦布尔分布 3～4 Gamma分布 2～4 对数分布 5～10 瑞利分布 2～4
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陈琪, 王娜, 陆军, 等. SAR图像港口区域舰船检测新方法[J]. 电子与信息学报, 2011, 33(9): 2132–2137 doi: 10.3724/SP.J.1146.2011.00018CHEN Qi, WANG Na, LU Jun, et al. A new method for ship detection in harbor region of SAR images[J]. Journal of Electronics &Information Technology, 2011, 33(9): 2132–2137 doi: 10.3724/SP.J.1146.2011.00018 文伟, 曹雪菲, 张学峰. 一种基于多极化散射机理的极化SAR图像舰船目标检测方法[J]. 电子与信息学报, 2017, 39(1): 103–109 doi: 10.11999/JEIT160204WEN Wei, CAO Xuefei, and ZHANG Xuefeng. PolSAR ship detection method based on multiple polarimetric scattering mechanisms[J]. Journal of Electronics &Information Technology, 2017, 39(1): 103–109 doi: 10.11999/JEIT160204 艾加秋, 齐向阳, 禹卫东. 改进的SAR图像双参数CFAR舰船检测算法[J]. 电子与信息学报, 2009, 31(12): 2881–2885 doi: 10.3724/SP.J.1146.2008.01707AI Jiaqiu, QI Xiangyang, and YU Weidong. Improved two parameter CFAR ship detection algorithm in SAR images[J]. Journal of Electronics &Information Technology, 2009, 31(12): 2881–2885 doi: 10.3724/SP.J.1146.2008.01707 胡炎, 单子力, 高峰. 基于Faster-RCNN和多分辨率SAR的海上舰船目标检测[J]. 无线电工程, 2018, 48(2): 96–100 doi: 10.3969/j.issn.1003-3106.2018.02.04HU Yan, SHAN Zili, and GAO Feng. Ship detection based on faster-RCNN and multi-resolution SAR[J]. Radio Engineering, 2018, 48(2): 96–100 doi: 10.3969/j.issn.1003-3106.2018.02.04 徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136–148 doi: 10.12000/JR16130XU Feng, WANG Haipeng, and JIN Yaqiu. Deep learning as applied in SAR target recognition and terrain classification[J]. Journal of Radars, 2017, 6(2): 136–148 doi: 10.12000/JR16130 KANG Miao, LENG Xiangguang, LIN Zhao, et al. A modified faster R-CNN based on CFAR algorithm for SAR ship detection[C]. 2017 International Workshop on Remote Sensing with Intelligent, Shanghai, China, 2017: 1–4. doi: 10.1109/RSIP.2017.7958815. 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 LZZO A, LIGUORI M, CLEMENTE C, et al. Multimodel CFAR detection in foliage penetrating SAR images[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(4): 1769–1780 doi: 10.1109/TAES.2017.2672018 熊开玲, 彭俊杰, 杨晓飞, 等. 基于核密度估计的K-means聚类优化[J]. 计算机技术与发展, 2017, 27(2): 1–5 doi: 10.3969/j.issn.1673-629X.2017.02.001XIONG Kailing, PENG Junjie, YANG Xiaofei, et al. K-means clustering optimization based on kernel density estimation[J]. Computer Technology and Development, 2017, 27(2): 1–5 doi: 10.3969/j.issn.1673-629X.2017.02.001 冷祥光, 计科峰, 宋海波, 等. 影响星载SAR舰船检测的关键因素[J]. 遥感信息, 2016, 31(1): 3–12 doi: 10.3969/j.issn.1000-3177.2016.01.001LENG Xiangguang, JI Kefeng, SONG Haibo, et al. Key factors influencing ship detection in spaceborne SAR imagery[J]. Remote Sensing Information, 2016, 31(1): 3–12 doi: 10.3969/j.issn.1000-3177.2016.01.001 NOVAK L M, OWIRKA G J, and NETISHEN C M. Performance of a high-resolution polarimetric SAR automatic target recognition system[J]. Lincoln Laboratory Journal, 1993, 6(1): 11–24. 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 张颢, 孟祥伟, 刘磊, 等. 改进的基于Parzen窗算法的SAR图像目标检测[J]. 计算机科学, 2015, 42(11A): 151–154ZHANG Hao, MENG Xiangwei, LIU Lei, et al. Improved parzen window based ship detection algorithm in SAR images[J]. Computer Science, 2015, 42(11A): 151–154 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 TIAN Sirui, WANG Chao, and ZHANG Hong. An improved nonparametric CFAR method for ship detection in single polarization synthetic aperetuer radar imagery[C]. IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 6637–6640. doi: 10.1109/IGARSS.2016.7730733. 张苗辉, 郭拯危, 刘扬. 基于混合模型的SAR影像海陆分割算法[J]. 光电子•激光, 2017, 28(3): 326–333 doi: 10.16136/j.joel.2017.03.0248ZHANG Miaohui, GUO Zhengwei, and LIU Yang. Sea-land segmentation algorithm for SAR images based on mixture models[J]. Journal of Optoelectronics•Laser, 2017, 28(3): 326–333 doi: 10.16136/j.joel.2017.03.0248 -
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