范数理论在尾流气泡幕参数研究中的应用

针对目前尾流测量中只能获得一个参数的缺点,本 文将范数理论应用于尾流气泡幕参数的计算。 试验中,通过片光源切片成像系统拍摄尾流气泡幕图像,应用范数计算方法结合粒径信息得 到气泡幕图像的 粒密度、粒径分布均匀性和体密度等参数,再通过上述参数判断产生尾流的目标航体的相关 信息。与传统的方法相比,范数计算方法具有所需已知量少、计算方法简单、获取信息量大 和 对舰船尾流的状态判断准确等优点,有望在舰船尾流气泡幕的研究取得重要的应用。     

气液两相流中改进图像互相关测速的完整实现

针对气液两相流中密集气(液)相速度场准确测量的需要,提出了改进图像互相关测速的完整实现方法。基于傅里叶变换的图像互相关测速原理,采用回归迭代互相关算法以提高测量精度,基于相邻速度属性对误矢量进行修正,并采用亚像素拟合方法准确定位,通过对不同拟合算法进行比较,验证了3点高斯拟合算法的优越性。实验结果表明,本文提出的测量方案可以有效提高图像互相关测速的精度。     

基于双目CCD测距的目标图像匹配算法研究

为了实现高精度的远距离双目CCD被动测距,提出改进的互相关匹配算法与三次曲面拟合算法,对双目CCD拍摄的两幅图像进行亚像素级匹配。介绍了双目测距系统的测距原理;进行测距图像的匹配操作,提出基于模板的灰度互相关算法,根据互相关运算的结果,提出亚像素级匹配算法,即采用三次曲面拟合方法,拟合出相关峰的亚像素级坐标;对测距实验结果进行误差分析,并修正系统误差的计算公式。实验表明,3 km以内的目标实际测量精度优于0.5%,满足高精度双目测距的精度要求。     

MEMS测量中一种新型的亚像素定位技术

亚像素定位技术是实现基于视觉的高精度MEMS测量的关键。文章提出了一种混合非线性优化和离散傅里叶变换的亚像素定位方法。首先通过相位相关法估计出像素级平移参数,然后利用互功率谱的整体信息,在其邻域范围内进行上采样矩阵傅里叶变换,精确定位相位互相关谱的峰值,测得亚像素级平移参数,最后实现了图像的高精度匹配。实验表明,该算法的定位精度优于扩展的相位相关法,像素定位精度能达到0.01像素,能够满足MEMS运动图像的高精度定位,且具有更好的噪声抑制能力和计算复杂度较低的特点。     

基于微粒子群优化算法的数字散斑图像相关方法

为了能够通过一步搜索同时得到数字散斑图像中所测点的整像素和亚像素位移信息,采用灰度插值的方法构造了亚像素子区,改进了基于微粒子群算法的数字图像散斑相关方法。对含有平移信息的模拟散斑图和具有应变的模拟散斑图进行相关计算,验证了该方法的适用性;在对具有微小面内位移转动的试件进行测量时,比较了整像素的微粒子群算法和不同量级的灰度插值下的亚像素微粒子群算法。结果表明,基于微粒子群算法的亚像素数字散斑图像相关方法在测量小位移方面具有一定的优越性。     

基于图像处理的建筑物振动位移测量算法

针对地震后高层建筑物结构损伤监测问题,该文提出一种基于方向码匹配(OCM)和边缘增强匹配(EEM)算法的微小位移测量算法。该算法先将原始图像梯度信息与像素强度融合,增强图像信息;采用相位相关法进行匹配运算,匹配速度比归一化互相关法提升了96.1%;最后使用亚像素插值法,使测量结果达到亚像素精度。实验结果表明,该文算法避免了OCM和EEM算法量化过程中图像梯度信息的损失,大大提高了模板匹配精度,匹配速度比OCM提升了43.3%,比EEM提升了19.6%。     

基于最小二乘互相关算法的图像定位匹配研究

提出了一种基于最小二乘互相关的图像定位匹配算法。该算法将图像互相关信息和最小二乘法结合实现图像定位匹配,匹配精度可以达到亚像素甚至1/100像素级,同时利用金字塔分层来提高定位匹配速度;通过本算法在印刷品质量自动化检测系统中的应用,验证了该算法的高精度与高速度特性。1     

一种改进的亚像素边缘检测方法

为满足测量系统的快速、高精度的图像测量要求,提出一种基于传统Zernike矩结合小波变换实现亚像素边缘检测的方法。算法先用小波模极大值原理对图像粗定位,再用Zernike矩算法对边缘进行亚像素定位,并用最大类熵阈值法计算阈值,实现图像的亚像素边缘检测。实验表明,该方法抗噪性能好,且检测精度更高,能达0.1~0.2个像素。     

基于SURF和矩阵乘法的超大规模遥感图像亚像素配准算法研究

图像配准是一项基本而又非常关键的图像预处理技术。在很多应用领域,要求配准精度达到亚像素级。现有的相位相关法具有精度高、计算简单等特点,但是随着图像规模的增大,计算付出的时间代价是巨大的。本文提出基于SURF和矩阵乘法相位相关法的超大规模遥感图像亚像素配准算法,采用化整为零的方法,首先把整幅图像划分成不同区域,其次使用改进的Canny算法进行边缘分割,去除无用信息,再次使用SURF算法提取特征,最后在关键点周围使用矩阵乘法相位相关估计图像亚像素偏移量。实验表明本文提出的算法不仅提高了算法运行速度,同时也解决了图像尺寸太大导致一般计算机无法处理的问题。并且由于矩阵乘法相位相关的良好抗噪声特性,因此即使存在噪声,算法仍然可以获得较高的亚像素偏移量估计精度。     

基于互信息的亚像素级立体视觉点匹配方法研究

在立体视觉测量中,为获得更高精度,往往需要将视差计算精确到亚像素级。将互信息理论引入双目图像配准并结合多分辨率技术实现亚像素级点匹配。采用Bouguet立体校正算法对左右图像进行极线校正,利用Harris角点探测器检测目标并将获取角点作为待匹配点,采用最大互相关法进行搜索确定像素级匹配点。然后对以左右匹配点为中心的20×20邻域图像进行插值并分别放大10倍和100倍,采用互信息方法先对低分辨率图像进行配准,再在高分辨率图像上进一步细化求精,结合像素级匹配的整数视差可得最终亚像素级视差。实验结果表明,该方法能将视差精度提高到0.01像素。     

基于遥感图像的舰船目标检测与参数估计方法

利用遥感图像进行舰船检测与监视的研究和技术开发在海洋遥感领域得到了快速发展,并成为遥感数据重要的海洋应用之一。当海洋遥感图像用于舰船尾迹检测时,就可及时有效地发现和提取舰船目标信息。文中利用光学遥感图像的海面舰船尾迹特征以及相关参数,估测舰船目标的速度与航向。分别通过灰度累积法和Radon变换法两种不同的尾迹长度检测方法,对尾流长度进行了提取,并利用尾流长度特征简单估测了舰船航速与航向。对实验结果和误差进行了分析,并对影响舰船尾流检测的因素进行了讨论。估算结果初步验证了对航速与航向估算方法的有效性     

Simulation of ship generated turbulent and vortical wake imaging by SAR

Synthetic Aperture Radar (SAR) imaging of ocean surface features is studied.The simulation of the turbulent and vortical features generated by a moving ship and SAR imaging of these wakes is carried out.The turbulent wake damping the ocean surface capillary waves may be partially responsible for the suppression of surface waves near the ship track.The vortex pair generating a change in the lateral flow field behind the ship may be partially responsible for an enhancement of the waves near the edges of the smooth area.These hydrodynamic phenomena as well as the changes of radar backscatter generated by turbulence and vortex are simulated.An SAR imaging model is then used on such ocean surface features to provide SAR images.Comparison of two ships‘simulated SAR images shows that the wake features are different for various ship parameters.     

一种基于全极化SAR数据广义多子视相干的海面船只目标检测方法

传统基于子视相干的检测算法,如2L-IHP（Two Looks Internal Hermitian Product）和Pol-IHP（Polarimetric Internal Hermitian Product）等,通常利用两个子视影像进行相干来检测海面弱小船只目标。但受子视影像个数的限制,无法大幅度地提高船海对比度,进而影响了检测精度。针对该问题,本文提出了一种基于全极化SAR的广义多子视相干检测算法,首先利用子视分解方法对全极化SAR数据进行处理得到多个子视全极化影像;接着,基于广义相似性参数（Generalized Similarity Parameter,GSP）定义这些子视影像间的相关矩阵和相干算子来计算相干图;然后,利用恒虚警率（Constant False Alarm Rate,CFAR）检测方法结合统计的相干图累积分布函数进行船只目标检测。通过实验,表明利用本文算法船海对比度随着子视影像个数的增加而得到大幅提高,从而减少了弱小船只目标的漏检,显著提高了船只目标检测精度。     

Use of the Dempster-Shafer algorithm for the detection of SAR shipwakes

An automatic detection algorithm (ADA) for synthetic aperture radar (SAR) ship wakes, based upon the Dempster-Shafer algorithm, is described and applied to a large set of SEASAT wake imagery. The SEASAT images were chosen specifically to test the limitations of the ADA. Images exhibiting multiple SAR ship wakes, short or faint wake features, striated ocean backgrounds, or the presence of other naturally occurring linear ocean features, constituted the majority of images. The ADA correctly classified the majority of the images, with misclassifications linked largely to very short or faint linear features. The ADA performed very well in striated ocean regions and in areas where the wake features were competing with naturally occurring linear ocean structure     

Sonar Measurements in Ship Wakes Simultaneous With TerraSAR-X Overpasses

A pilot experiment was conducted in the period from April to June 2008 in the Straits of Florida near Port Everglades, Florida, in order to study the dynamics of far wakes of ships. In this experiment, a small boat with downward-looking sonar made “snakelike” sections through wakes of ships of opportunity during the TerraSAR-X overpasses. The ship and its parameters (length, speed, course, etc.) were identified utilizing an automated identification system. The sonar responded to the clouds of microbubbles generated in the ship wake by the propulsion system and ship-hull turbulence. The ship wakes were traced in the sonar signal typically from 10 to 30 min after the ship's passage. A preliminary analysis of the measurements suggests that the visibility of the centerline ship wake in synthetic aperture radar (SAR) images is correlated with the presence of microbubbles in the wake. This supports the hypothesis that natural surfactants scavenged and brought to the surface by rising bubbles play an important role in the wake visibility in SAR. The influence of the wind-wave field on the ship wake, as well as the effect of screening of the wind-wave field by the ship's hull, adds another level of complexity to wake patterns observed in SAR images.      

舰船尾流红外图像边界检测方法

由于舰船尾流与周围未扰动的海水相比温差较小且无明显轮廓,导致舰船尾流红外图像成像模糊、对比度低,难以提取其边缘信息。文中根据舰船尾流及其红外图像的特点,提出了一种利用动态纹理和数学形态学检测尾流边界的方法。首先,利用动态纹理分割方法对舰船尾流红外图像序列进行了预分割。然后,采用Canny算子对尾流区进行边缘提取。对提取的边缘利用形态学的方法进行膨胀、标记和选择,提取了真正的尾流边界。实验结果表明:该方法对舰船尾流红外图像序列进行处理,能够有效的提取尾流的边界,取得了较好的处理效果。     

尾流的噪声信号特性及其模拟研究

尾流的延伸范围大,其声学特性是进行尾流探测、跟踪和识别的信息宿。在研究尾流的产生机理和物理特性基础上,主要研究了尾流中气泡群的声学特征、由船舶螺旋桨造成的尾流信号的周期性特征。采用AR模型自相关谱估计法研究了模拟产生的船舶尾流信号的谱特征,分析和总结了尾流信号的源特性。建立了的尾流信号时域和频域数学模型,为尾流声学特性的工程应用,提供理论依据。     

SAR图像舰船及其尾迹检测研究综述

近年来,利用合成孔径雷达(SAR)图像进行舰船检测的研究在海洋遥感领域得到高度重视.本文回顾了SAR图像舰船及其尾迹检测的起源与发展,分析了影响舰船和尾迹的物理因素,分别对舰船检测算法和尾迹检测算法进行综述、总结和对比,并对今后的研究发展方向进行展望.     

An automatic ship and ship wake detection system for spaceborne SARimages in coastal regions

An automatic ship and ship wake detection system for spaceborne SAR images is described and assessed. The system is designed for coastal regions with eddies, fronts, waves and swells. The system uses digital terrain models to simulate synthetic SAR images to mask out land areas. Then a search for ship targets is performed followed by wake search around detected ship candidates. Finally, a homogeneity test and wake behavior test are performed which reduces the number of false alarms substantially. The system is demonstrated with ERS-1 SAR images and its performance is assessed using Seasat and ERS-1 images. No other information about the ships was available, hence, the basis for the assessment is through comparison with human visual interpretation of the same data. The number of lost ships (ship-like targets) was 7-8% for both Seasat-A and ERS-1. No false ships were detected. The number of lost or false wakes (wake-like features) was higher in ERS-1 images than in Seasat-A images and was nearly 15%. Taking into account the extremely strong variations in sea state in some of the selected scenes, the automatic detection performance is considered to be very good. In addition, the requirement of analyzing a 3-look ERS-1 scene of 100 km×100 km in less than eight minutes has been achieved on a workstation     

SAR图像非线性分布目标检测方法研究

在一定条件下,遥感图像上可以观测到舰船的尾迹或自然界的内波现象。目前对舰船尾迹的检测多集中在线性尾迹的检测上,该文提出一种基于生理视觉的静态边界轮廓系统和移动窗口形态滤波非线性分布目标检测方法,并且转换成二值图像后,运用二值形态滤波对线状要素进行分类,将非线性特征进行区分,以便进一步的处理。文中仿真和实际SAR图像试验的结果表明该算法是有效的。