﻿ 基于傅里叶变换的SAR图像舰船尾迹检测
 舰船科学技术  2023, Vol. 45 Issue (23): 186-189    DOI: 10.3404/j.issn.1672-7649.2023.23.035 PDF

Ship wake detection in SAR images based on Fourier transform
XIA Yun-qing
School of Basic Science, Zhengzhou University of Technology, Zhengzhou 450044, China
Abstract: Fourier transform based ship wake detection method in SAR images is studied, and the accurate ship wake detection results are used to provide the basis for ship speed and course information inversion. By using fractional Fourier transform method, the ship wake SAR image is transformed by linear integral transformation and plane rotation, and the ship wake SAR image is divided into high frequency subband and low frequency sub-band. Radon transform method is used to accumulate the gray values of linear pixels in the low frequency sub-band of SAR images, obtain the geometric correspondence between pixels and ship wake lines, search the peak points in Radon transform domain, and obtain the final detection results of ship wake lines in SAR images. Experimental results show that this method can effectively detect different types of ship wakes such as Calvin's wake and Bragg's wake in SAR images, and has superior ship wake detection effect.
Key words: Fourier transform     SAR images     ships     wake detection     low frequency sub-band     Radon transform
0 引　言

1 SAR图像舰船尾迹检测方法 1.1 SAR图像舰船尾迹分析

1.2 分数阶傅里叶变换的SAR图像高低频划分

 ${X_p}\left( u \right) = \int_{ - \infty }^{ + \infty } {{K_p}\left( {u,t} \right)x\left( t \right)} {\mathrm{d}}t，$ (1)

 ${X_1}\left( u \right) = \int_{ - \infty }^{ + \infty } {x\left( t \right){e^{ - 2j \text{π} ut}}} {\mathrm{d}}t 。$ (2)

 ${\boldsymbol{M}} = \left[ {\begin{array}{*{20}{c}} {\cos \alpha }&{\sin \alpha } \\ { - \sin \alpha }&{\cos \alpha } \end{array}} \right]，$ (3)

 $\left[ {\begin{array}{*{20}{c}} u \\ v \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} {\cos \alpha }&{\sin \alpha } \\ { - \sin \alpha }&{\cos \alpha } \end{array}} \right]\left[ {\begin{array}{*{20}{c}} t \\ f \end{array}} \right] = {\boldsymbol{M}}\left[ {\begin{array}{*{20}{c}} t \\ f \end{array}} \right]。$ (4)

 $l = x\cos \varphi + y\sin \varphi ，$ (5)

 ${R_{l,}}_\varphi = \sum\limits_{x = 1}^M {\sum\limits_{y = 1}^N {\beta f\left( {x,y} \right)\left( {l - x\cos \varphi - y\sin \varphi } \right)} } 。$ (6)

2 实例分析

 图 1 原始舰船尾迹SAR图像 Fig. 1 SAR image of the original ship wake

 图 2 舰船尾迹图像低频子带 Fig. 2 Low frequency subband of ship wake image

 图 3 低频子带对应频谱图 Fig. 3 Frequency spectrum diagram of low frequency subband

 图 4 舰船尾迹线检测结果 Fig. 4 Ship wake line test results

 图 5 舰船尾迹真值 Fig. 5 True value of ship wake

 图 6 傅里叶描述子对比结果 Fig. 6 Comparison results of fourier descriptors

3 结　语

 [1] 王彬, 王国宇. 一种PCNN和边缘检测协同的SAR图像分割算法[J]. 现代防御技术, 2021, 49(3): 92-97+122. WANG Bin, WANG Guoyu. SAR Image Segmentation Algorithm Based on PCNN and Edge Detection[J]. Modern Defense Technology, 2021, 49(3): 92-97+122. DOI:10.3969/j.issn.1009-086x.2021.03.012 [2] 张冬冬, 王春平, 付强. 基于特征增强网络的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2023, 45(4): 1032-1039. ZHANG Dongdong, WANG Chunping FU Qiang. Ship target detection in SAR image based on feature-enhanced network[J]. Systems Engineering and Electronics, 2023, 45(4): 1032-1039. [3] 胡欣, 马丽军. 基于YOLOv5的多分支注意力SAR图像舰船检测[J]. 电子测量与仪器学报, 2022, 36(8): 141-149. HU Xin, MA Lijun. Multi-branch attention SAR image ship detection based on YOLOv5[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(8): 141-149. DOI:10.13382/j.jemi.B2205545 [4] 孙忠镇, 戴牧宸, 雷禹, 等. 基于级联网络的复杂大场景SAR图像舰船目标快速检测[J]. 信号处理, 2021, 37(6): 941-951. SUN Zhongzhen, DAI Muchen, LEI Yu, et al. Fast Detection of Ship Targets for Complex Large-scene SAR Images Based on a Cascade Network[J]. Journal of Signal Processing, 2021, 37(6): 941-951. DOI:10.16798/j.issn.1003-0530.2021.06.005 [5] 闫佳楠, 聂丁, 张民. 转弯航行舰船开尔文尾迹散射特征研究[J]. 中国舰船研究, 2023, 18(4): 129-139. YAN Jianan, NIE Ding, ZHANG Min. Study on Kelvin wake scattering characteristics of turning ship[J]. Chinese Journal of Ship Research, 2023, 18(4): 129-139. [6] 成艳, 于雪莲, 钱惟贤, 等. 红外遥感图像舰船尾迹提取及检测[J]. 红外与激光工程, 2022, 51(2): 32-39. CHENG Yan, YU Xuelian, QIAN Weixian, et al. Ship wake extraction and detection from infrared remote sensing images[J]. Infrared and Laser Engineering, 2022, 51(2): 32-39.