顺轨机载InSAR海面运动舰船成像

研究了顺轨双天线机载干涉合成孔径雷达（InSAR）的海面运动舰船目标成像问题.分析了船体散射点三维运动对舰船目标成像的影响,给出了顺轨InSAR对舰船目标成像的信号处理流程.利用双通道干涉对消处理,检测出存在三维运动的散射点,并对其信号相位进行估计.通过对存在三维运动的散射点实施相位补偿,改善对舰船目标成像效果,InSAR仿真数据的处理结果表明了本文方法的有效性.     

联合数学规划策略和精细极化分解的极化SAR舰船目标检测

由于人造目标具有方位多变和结构复杂等特点,设计具有强泛化、易分辨的特征来实现极化合成孔径雷达(synthetic aperture radar, SAR)目标精准检测是一个非常有挑战性的问题.受数学规划思想的启发,在基于物理模型的极化分解框架下,本文设计了一种散射贡献组合特征检测器用于检测极化SAR舰船目标.一方面,利用类差倒数变换和sigmoid函数变换,首先构造了一种旋转二面角散射模型,它能充分反映由方位变化所导致的同极化和交叉极化响应之间的快速转变.结合类偶极子混合散射模型,以及通过设计基于根的判别式的模型求解策略,随后提出了一种极化信息完全利用的八成分目标分解方法,它能够有效刻画舰船目标的局部结构散射并显著改善体散射过估问题.另一方面,通过分析舰船目标不同散射机理的分布显著特性,以及舰船目标相对海杂波的散射独有特性,利用线性及非线性运算等数学规划策略,首先设计了一种稳健的散射贡献组合器特征.随后将该特征与保护滤波器结合,进一步增强其分辨能力以实现最终的舰船目标检测.实验利用不同实测极化SAR数据对所提方法进行了定性和定量验证,结果表明该方法优于现有的其他舰船目标检测方法,检测品...     

星载微波散射计的在轨定标技术现状及展望

星载微波散射计通过测量海面不同方位向的后向散射系数反演出海面的风速和风向,为了满足测量精度要求,必须对其进行在轨定标.本文总结了现有微波散射计的在轨定标技术,包括有源定标器法、陆地自然目标法、海洋目标法和星星交叉定标法;然后分析了每种定标方法的优缺点;最后,描述了现有定标方法适用扇形波束体制散射计和笔形波束圆锥扫描体制散射计的特性.     

遥感图像中复杂海面背景下的海上舰船检测

针对遥感图像中复杂海面背景下海上舰船的检测问题展开讨论,在Itti视觉显著度模型的基础之上进行改进,提出一种基于特征显著度图的复杂海面上舰船的自动检测方法,解决了传统的阈值分割方法在遥感图像复杂海面背景下较难将目标与背景分离的问题.在多种不同复杂海面背景下的舰船检测实验中,与传统阈值分割方法比较,本文方法有较高的检测率和较低的虚警率.     

基于显著性的遥感图像舰船目标检测

舰船作为海洋信息感知中的重要目标,其检测在军舰探测、精确制导等军用领域以及海面搜救、渔船监测等民用领域具有极其重要的战略意义.海洋遥感图像受云雾、风浪、海杂波和光照等干扰使得舰船检测具有挑战性.根据可见光遥感图像舰船目标检测特点提出粗检测和细鉴别相结合的技术路线.先基于视觉显著性的谱残差法对图像进行增强以提取目标候选区域,后根据舰船与干扰因素差异采用舰船方向梯度直方图特征对目标候选区域进行鉴别,提取真正的舰船目标.实验结果表明,上述算法舰船检测率高,对光照、海杂波干扰具有一定程度的鲁棒性,且能有效剔除碎云岛屿等干扰物,显著降低虚警率.     

光学遥感图像舰船目标检测与识别综述

遥感图像舰船目标自动检测与识别是遥感图像处理与分析领域备受关注的课题, 其核心任务是判断遥感图像中是否存在舰船目标,并对其进行检测、分类与精确定位, 它在海面交通监控、船只搜救、渔业管理和海域态势感知等领域具有广阔的应用前景. 本文主要围绕光学卫星遥感图像中的舰船目标自动检测与识别, 分析舰船目标检测与识别面临的难点问题, 综述当前光学遥感图像舰船检测与识别的主要处理方法, 在此基础上指出研究中尚存在的问题并展望未来的发展趋势.     

联合显著性特征与卷积神经网络的遥感影像舰船检测

目的 针对高分辨率遥感影像舰船检测受云雾、海浪以及海岛等复杂因素干扰,存在虚警率高、漏检率高、目标检测和识别困难等问题,提出一种联合视觉显著性特征与卷积神经网络的海面舰船目标检测方法。方法 基于频率域相位谱显著性检测能够有效抑制高分辨率遥感影像上云层、海面杂波干扰的特点,计算影像多尺度显著图并进行加权融合。采用对数变换对融合后的图像进行空间域灰度增强以提高目标与背景的区分度,利用灰度形态学闭运算填充舰船目标孔洞,采用大津分割法来提取疑似舰船目标作为兴趣区域。最后构建舰船样本库,利用迁移学习的思想训练卷积神经网络模型,对所有兴趣区域切片进行分类判断和识别,得到最终检测结果。结果 利用多幅不同背景下的高分辨率遥感影像,分别从视觉显著性检测、舰船粗检测与船只类型识别3个方面进行实验验证,选取检测率、虚警率、识别率3个指标进行定量评价。结果表明,本文方法相比于其他方法能有效排除云雾、海岛等多种因素的干扰,检测率、虚警率、识别率分别为93.63%、3.01%、90.09%,明显优于其他算法,能够实现大范围影像上多种类型舰船的快速准确检测和识别。结论 本文将图像视觉显著性检测快速获取图像显著目标的特点与卷积神经网络在图像分类的优势相结合,应用于遥感影像的海域舰船目标检测,能够实现对复杂背景下舰船目标的检测和船只类型的精细化识别。     

盲源分离与高分辨融合的DOA估计与信号恢复方法

目标方位估计(Direction of arrival, DOA)和信号恢复分别是水下目标定位、跟踪与识别的前提.基于盲源分离方法可以得到含有阵列流形信息的解混矩阵, 融合成熟的高分辨方法提出了一种新的方位估计、信号恢复模型和方法. 在宽带信号背景下进行了仿真实验, 结果表明该方法可实现目标方位的实时估计和目标信号的恢复. 在同等条件下完成同样的目标方位分辨率, 比单纯的高分辨方法要求的阵元数和快拍数较少, 要求的信噪比要低. 海上实测数据检验也表明, 比常规的最小方差无失真响应(Minimum variance distortionless response, MVDR)方法得到了更好的结果, 明显提高了弱目标信号的空间谱能量, 增强了检测弱目标信号的能力.     

基于海情和蒸发波导的舰船目标 RCS 研究

由于海面自身的条件及随机多变的特性,目标雷达散射回波会受到不同程度的影响。分别研究了不同海情、蒸发波导以及两种情况共同制约的状态下,舰船目标雷达散射回波情况,并进行了理论分析和岸-舰情况仿真。结果表明：①随海情等级的增加,目标 RCS 减小;②随着波导高度的增加,目标 RCS 增大,且大气湿度对蒸发波导传播有一定影响;③海面的粗糙度会影响蒸发波导传播,随粗糙度的增大,目标 RCS 会变小。研究结果为海面雷达侦测提供了参考依据。     

一种港口区域舰船目标变化检测新方法

基于同一地区不同时相港区遥感图像,在图像感兴趣区域(ROI)目标检测的基础上,研究了一种港区舰船目标变化检测新方法。该方法检测和识别港区舰船目标,比较目标位置变化后,给出了定量分析结果。提出了一种用经纬度计算目标距离的算法,可以克服多幅图像摄取环境、摄取时差等干扰因素的影响;将舰船距离和舰船斜边2个参数综合成斜距,简化舰船目标的变化分析。     

Fully Polarimetric SAR Ship Detection based onPCDM Shannon Entropy#br#

Polarimetric Synthetic Aperture Radar（PolSAR）data contains rich polarization information about the scattering properties of ground objects,having beenwidely used in maritime monitoring and objects detection.The polarization reaction differences between ship targets and sea clutters are analyzed.A ship detection method using the Shannon entropy of the Polarimetric Covariance Difference Matrix (PCDM) is proposed in this paper,which is applied to fully polarimetric SAR images.To enhance the contrast between the ship targets and sea background,the PCDM is generated by calculating the elemental differences between the polarimetric covariance matrix at each pixel and its neighbors.Then the Shannon entropy of SAR images are extracted on the basis of the Shannon entropy calculation formula,and the character difference between the ships and background in the Shannon entropy map is presented for ship detection.The false alarms in the detection result caused by the azimuth ambiguities are removed,based on the displacement distance and energy ratio relationship,between the target and azimuth ambiguity.The Radarsat\|2 Fine Quad data and the Chinese GF\|3 Quad\|Polarimetric Stripmap Ⅰ data are used,to verify the effectiveness of the proposed method,and the SPAN method,HV channel image and polarimetric whitening filter (PWF) method are applied for comparison.The detection and comparison results indicate that the proposed method is able to effectively enhance the ship\|sea contrast,and has higher detection accuracy.     

星载SAR影像上船舶方位向模糊去除算法

目的 掌握海上船舶分布状态对于海上交通流分析和通航安全管理具有重要作用。遥感技术,特别是星载合成孔径雷达(SAR)技术的发展,为大范围海上船舶检测提供了有效的手段,但受SAR成像机制影响,海上船舶目标在星载SAR影像上通常存在着不同程度的方位向模糊噪声,这些噪声易被误判为船舶,导致船舶识别中虚警率提高。方法 本文简述了方位向模糊噪声的产生原因,提出了一种新的星载SAR影像上船舶方位向模糊去除算法,该算法的核心是构建目标方位向角度一致性、方位向位置偏移距离和方位向模糊能量衰减3个判别规则,对潜在SAR影像亮斑目标进行逐层筛选,实现船舶真实目标和方位向模糊目标的判别。结果 选取中国渤海海域和黄海海域的30 m分辨率的Radarsat-2数据进行案例分析,并与船舶自动识别系统(AIS)实测数据进行比对校验,结果表明,传统的双参数恒虚警率(CFAR)算法和基于K分布的CFAR等算法对于船舶难以剔除方位向模糊,容易造成虚警,而本文算法对实验影像的船舶方位向模糊去除准确率优于95.8%,能够有效剔除船舶方位向模糊。结论 该算法为星载SAR影像上船舶方位向模糊去除提供了新的手段,有助于提高SAR影像上船舶目标检测的准确性。     

高分辨率遥感图像港内舰船的自动检测方法

在高分辨率的光学遥感图像中,港口内停靠舰船的灰度、纹理特征与陆地接近,因此同行驶于海上的舰船相比,是较难实现自动检测的目标.考虑到港口几何布局稳定的特点,提出了一套基于地理信息的舰船目标检测算法.该算法结合矢量数据和栅格数据的优点,采用模板形式存储地理信息,并以自动阈值法进行目标的粗分割,充分利用先验信息和相关知识完成并联目标的切割和断裂目标的连接.通过仿真实验,证明该算法能准确、快速的实现港内舰船的自动检测.     

改进Notch滤波的全极化SAR数据船舶检测方法

全极化SAR数据提供了更多的地物极化散射信息,目前被广泛的应用于海上船舶检测的应用研究。本文提出改进的Notch滤波方法,实现全极化SAR数据的海上船舶检测。该方法结合目标的极化散射特性与能量双重特点,设计针对海面、方位向模糊、相干斑噪的不同滤波,消除虚警,通过SPAN能量因子降低由于散射机制相同而造成的漏检。利用RADATSAT-2全极化精细扫描数据对本文的算法进行验证,并与PWF和SPAN方法进行对比分析,实验结果表明本文提出的方法能从海面上有效检测出各种大小的船舶,同时能抑制方位向模糊、相干斑噪以及船舶的旁瓣造成的虚警。     

基于极小误差阈值分割的舰船自动检测方法

针对中、高分辨率(0.6m ~5m)可见光卫星遥感图像，提出了一种基于极小误差分割的海上舰船自动检测算法，该方法利用自适应定向正交投影的高斯分解法，拟合海洋区域直方图，采用改进的信息熵极小误差分割算法，确定分割阈值。对于候选舰船目标采用基于多判据的目标检测思想检测舰船，对100多幅可见光图像进行了试验。结果表明，该方法能够自动、快速、准确地检测图像中的舰船，具有较高的检测能力。     

A new method for extracting ship motion parameters in Radarsat-2 SAR imagery

Traditionally, moving ship detection by Synthetic Aperture Radar (SAR) image is primarily based on the ship wake feature. However, many ship wakes cannot be imaged by SAR owing to changes in imaging conditions, such as the SAR band, polarisation, incident angle, and sea state. In this study, we discovered a unique phenomenon called ‘azimuth tail’ from Radarsat-2 SAR images. Following research and analysis, we determined that the azimuth tail is not manifested as a visible disturbance on sea surface waves. Instead, it is an observation enabled by certain SAR imaging principles. Consequently, we propose a new method for extracting information on moving vessels after a preliminary analysis of the principle of the azimuth tail. The results of experimental analysis of the correctness of the method indicate that the error of the vessel’s velocity from its azimuth tail is less than 20%, and the azimuth tail can be applied to the detection of moving vessels in oceans using Radarsat-2 SAR imagery.     

A New Ship Target Detection Algorithm based on SVMin High Resolution SAR Images

The characteristics of ocean background and target in the high resolution synthetic aperture radar (SAR) images are analyzed.Aiming at the requirements of ship detection in high-resolution synthetic aperture radar (SAR) image,the detection accuracy,intelligence level,real-time and processing efficiency,we put forward a high resolution SAR images ship detection algorithm based on support vector machine.The algorithm designs a pre-training support vector machine (SVM) classifier and complete the screening of the ship target block area,then the algorithm of optimal entropy thresholds proposed by Kapur,Sahoo,Wong(KSW) will be used on the target area selected for fine detection of ship targets.In this paper,several commercial satellite data,such as TerraSAR-X,are used to verify the experiment.Comparing with the classical CFAR detection algorithm,Experimental results show that the algorithm can improve the false alarm caused by the speckle noise and ocean clutter background inhomogeneity.At the same time,the detection speed is also increased by 20% to 35%.     

Removal of azimuth ambiguities and detection of a ship: using polarimetric airborne C-band SAR images

Synthetic aperture radar (SAR) imagery from the sea can contain ships and their ambiguities. The ambiguities are visually identifiable due to their high intensities in the low radar backscatter background of sea environments and can be mistaken as ships, resulting in false alarms in ship detection. Analysing polarimetric characteristics of ships and ambiguities, we found that (a) backscattering from a ship consisted of a mixture of single-bounced, double-bounced and depolarized or diffused scattering types due to its complex physical structure; (b) that only a strong single- or double-bounce scatterer produced ambiguities in azimuth that look like relatively strong double- or single-bounce scatterers, respectively; and (c) that eigenvalues corresponding to the single- or double-bounce scattering mechanisms of the ambiguities were high but the eigenvalue corresponding to the depolarized scattering mechanisms of the ambiguities was low. With these findings, we proposed a ship detection method that applies the eigenvalue to differentiate the ship target and azimuth ambiguities. One set of C-band JPL AIRSAR (Jet Propulsion Laboratory Airborne Synthetic Aperture Radar) polarimetric data from the sea have been chosen to evaluate the method that can effectively delineate ships from their azimuth ambiguities.     

PolSAR ship detection based on the contrast enhancement of polarimetric scattering differences

The ship detection in polarimetric synthetic aperture radar (PolSAR) mode is a hot topic in recent years, because of the diversity of polarimetric scattering mechanisms between ship targets and sea clutter. To improve the detection performance of ship targets, this paper mainly develops the ship detection method based on the contrast enhancement utilizing the polarimetric scattering difference. The algorithm first enhances the target signal utilizing the scattering difference of the polarimetric coherency matrix between ship targets and sea clutter, and then a simple threshold is applied to distinguish the ship targets from the sea clutter. Finally, real PolSAR datasets recorded by AirSAR system are used to evaluate the effectiveness of the proposed detection method. Compared with other detection methods, experimental results indicate that the proposed method can effectively improve the detection performance of ship targets.     

一种基于矩不变的SAR海洋图像舰船目标检测算法

提出了一种先分离SAR图像场景中的陆地和海洋区域，再利用矩不变自动门限法对分离出的海洋子场景进行舰船目标检测的算法。对数据处理的结果表明，该方法能够有效、快速、准确地检测到SAR海洋图像中的舰船目标。