基于Fourier-Mellin变换和相干系数法的重复轨道干涉SAR图像配准新方法

相对于单轨双天线InSAR系统,重复轨道InSAR系统由于轨道不能完全平行,导致InSAR图像对间存在夹角,使其配准更为困难。该文针对非平行重复轨道的InSAR问题,提出了一种结合Fourier-Mellin变换和相干系数法的InSAR图像配准算法,对存在夹角的SAR复图像对进行精确配准。该方法用于粗配准时,通过引入非线性图像幅度变换预处理,克服了Fourier-Mellin变换无法直接处理SAR图像对幅度分布动态范围大且不均匀的问题,而用于精配准时,通过引入相干系数法,精确估计了复图像对之间存在的微小角度。以SIR-C/X重复轨道的X波段SAR数据为例,详细比较了传统相干系数配准算法和该文算法在配准精度和计算时间上的差异,从而验证了算法的有效性。     

星载InSAR复图像多级配准及其误差分析

复图像的配准是星载干涉合成孔径雷达(InSAR)数据处理中的重要步骤。对于星载InSAR复图像的配准,为了达到高的配准精度并同时保持较高的计算效率,需要采用多级配准的方法,即可先后采用几何配准方法、基于窗口的配准方法和基于频谱差别的配准方法来完成复图像的配准。首先讨论了三种算法的原理和处理方法,其中详细分析了基于频谱差别配准方法的原理。然后分析了各算法的误差,由于基于频谱差别配准影响最终的配准精度,分析了通过去噪来减少其误差。使用上述算法对仿真数据的处理结果验证了算法的有效性。     

基于最少残余点数的InSAR复图像配准

提出了一种基于最少残余点数法的干涉合成孔径雷达(InSAR)复图像配准新算法。通过分析引起干涉相位图残余点的因素,以及残余点与相位图的信噪比和平均波动函数之间的内在联系,提出可以用相位图的残余点数最少作为InSAR复图像配准的评价测度。新算法的计算量比传统的最大干涉频谱法要小而且更稳定,与平均波动函数法相比则计算量相当但不受相位跳变的影响。试验结果验证了新算法的有效性。     

一种针对多通道GMTI的SAR复图像精确配准方法

提出了一种针对多通道GMTI的SAR复图像精确配准算法。首先,系统分析了配准误差对干涉相位的影响,推导出了对应配准误差的干涉相位Cramer-Rao界,并利用Monte Carlo仿真数据对配准误差的影响进行了量化。在此基础上,通过对待配准图像进行二维精确插值处理,生成模板图像库和相应的相关系数库,以适合多通道GMTI的复图像的相位相关为准则,找出模板图像库和相关系数库中与参考图像具有最大相位相关值的图像作为配准图像,从而实现多通道SAR复图像之间的精确配准。对基于所提算法的实测三通道SAR复图像进行慢动目标检测实验,结果表明所提算法能够提供多通道GMTI所需要的配准精度,证明了该算法的有效性和实用性。      

基于SURF-DAISY算法和随机kd树的快速图像配准

提出了一种基于SURF-DAISY算法和随机kd树的快速图像配准方法,首先利用经典SURF特征检测器分别提取参考图像和待配准图像中的特征点,为了进一步提高算法速度,对SURF算法进行改进,利用DAISY描述符代替SURF原有的特征描述算法对特征点进行描述;之后通过随机kd树算法对参考图像和待配准图像的特征点进行匹配并且使用经典RANSAC(随机抽样一致性)算法剔除误匹配点对;最后用最小二乘法估算出最佳的空间几何变换参数,实现两幅图像的配准。实验结果表明:相对于标准的SURF算法,本文方法在基本保持性能不变的同时,配准过程所消耗的时间最多减少了45.6%。     

一种准自动高精度图像配准算法

为了增强图像配准的鲁棒性，提出了一种人机交互的图像配准算法，该算法首先利用人工选择控制点的方式得到变化模型的初始参数，然后利用初始参数寻找其他可能匹配的特征点对，最后利用加权最小二乘法精确求解变化模型的参数，并完成图像的配准。该算法不仅降低了计算量，而且减少了误配的可能性，并且对灰度差异较大的图像也是适用的。大量实验表明，提出的方法对同种传感器或者不同传感器图像之间的配准都是有效的。     

一种基于改进SURF的遥感图像自动配准方法

现有的遥感图像自动匹配方法存在匹配准确度低、计算量大、配准效率低的问题,在经典SURF算法基础上,提出一种改进的SURF算法,首先提取遥感图像放射不变闭合区域,然后利用SURF算法提取该区域内的特征点,建立特征点筛选机制剔除信息含量低且分布不均匀的特征点,最后结合最小二乘法完成图像自动配准。研究结果表明,该配准方法在提高配准精度的同时,减少了运算时间,提高了算法的整体性能。     

基于改进SURF算子的高低分辨率图像配准方法

针对激光三维成像传感器与可见光传感器图像分辨率差异较大,配准过程中特征点误匹配情况严重的问题,提出了一种基于改进SURF算子的高低分辨率图像配准方法。首先,采用双线性插值算法对低分辨率图像进行预处理,然后在经典SURF算子的基础上,采用最近邻向量匹配法完成SURF特征的粗匹配,并基于特征偏移一致性原则对匹配情况做进一步优化,最后结合RANSAC和最小二乘法求出图像之间的仿射关系,利用所求的变换参数插值得到配准后的图像。实验结果表明,该配准方法在保持配准速度的同时,结构相似性测量指数提高了约11%,进一步提高了配准的精度。     

欺骗干扰对INSAR复图像配准的影响分析

干涉式合成孔径雷达(INSAR)利用SAR图像对获得观测区域的高程信息,其准确性与SAR图像对的相关性有着密切的联系.受到干扰的SAR图像对间相关性会发生变化,这将直接影响INSAR的成像处理结果.该文首先阐述了间歇采样转发干扰、弹射式干扰的原理,分析了其对主辅天线回波信号相位的影响;接着计算机仿真了两种干扰条件下的复图像对配准情况;然后通过配准相关系数特性及干涉相位图质量分析了两种干扰方式对INSAR复图像对配准的影响,结论如下:间歇采样转发干扰能够破坏复图像对的相关性;弹射式干扰条件下,SAR图像对仍能保持较高的相干性,但SAR图像对配准后形成的干涉相位纹图所代表的地形与真实地形存在较大差异,即与间歇采样转发干扰相比,弹射式干扰对InSAR具有更强的欺骗效果.     

融合显性和隐性度量的多模图像配准算法

为解决基于隐性度量的图像配准算法初始配准误差大容易引起失配的问题,本文提出了一种融合显性度量和隐性度量的多模图像分层配准算法。首先采用金字塔分解算法得到低分辨率待配准图像。然后在每一层金字塔图像中,先采用互信息作为显性度量,利用粒子群算法获得初始的配准参数;以此作为初始值,采用基于隐性度量的配准算法,利用融合粒子群和鲍威尔搜索法的优化算法获取更准确的配准参数。基于低分辨率图像计算得到配准参数后,先对高分辨率待配准图像进行变换,然后利用提出的上述算法进行参数优化,根据每层得到的配准参数计算最终的配准参数。最后,采用可见光与红外图像、多波段SAR图像进行了配准实验。实验结果表明,提出的算法适用于多模图像配准,能够减小配准误差,具有一定的适用性。      

基于DEM辅助后向投影模型的InSAR高程反演方法

利用干涉合成孔径雷达(InSAR)技术获取数字高程模型(DEM)时,在地形起伏剧烈区域,干涉条纹十分密集,增加了相位解缠的难度,影响相位展开和高程反演的精度.为了解决该问题,该文提出了一种基于DEM辅助后向投影模型的InSAR高程反演方法.该方法可以在统一的后向投影成像空间中实现成像和InSAR高程反演,并且引入外源D...     

Multimodal registration of retinal images using self organizing maps

In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the self organizing maps and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration.     

Key technology of D-InSAR at X-band for monitoring land subsidence in mining area and its application

In theory, land subsidence measurement results with high accuracy can be obtained by using the Differential Interferometry Synthetic Aperture Radar （D-InSAR） at X-band. In practice, however, the measuring accuracy of D-InSAR at X-band has been seriously affected by some factors, e.g., decorrelation and high deformation gradient. In this work, the monitoring capability of D-InSAR for coal-mining subsidence is evaluated by using SAR data acquired by TerrraSAR-X system. The SAR image registration method for low coherence image pairs, the denoising phase filter for high noise level interferogram and atmospheric effects mitigation method are the key technical aspects which directly influence the measurement results of D-InSAR at X-band. Thus, a robust image registration method, an improved phase filter method and an atmospheric effects mitigation method are proposed in this paper. The proposed image registration method successfully achieves InSAR coregistration, while the amplitude cross-correlation cannot properly coregister low coherence SAR image pairs. Moreover, the time complexity of the proposed image registration method is obviously slighter than that of the Singular Value Decomposition （SVD） method. The comparing experiment results and the unwrapping phase results show that the improved Goldstein filter is more effective than the original Goldstein filter in noise elimination. The atmospheric influence correction experiment results show that the land subsidence areas with atmospheric influence correction are more clarified than that of without at- mospheric influence correction. In summary, the presented methods directly improved the measurement results of D-InSAR at X-band.     

Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery

Intraoperative freehand three-dimensional (3-D) ultrasound (3D-US) has been proposed as a noninvasive method for registering bones to a preoperative computed tomography image or computer-generated bone model during computer-aided orthopedic surgery (CAOS). In this technique, an US probe is tracked by a 3-D position sensor and acts as a percutaneous device for localizing the bone surface. However, variations in the acoustic properties of soft tissue, such as the average speed of sound, can introduce significant errors in the bone depth estimated from US images, which limits registration accuracy. We describe a new self-calibrating approach to US-based bone registration that addresses this problem, and demonstrate its application within a standard registration scheme. Using realistic US image data acquired from 6 femurs and 3 pelves of intact human cadavers, and accurate Gold Standard registration transformations calculated using bone-implanted fiducial markers, we show that self-calibrating registration is significantly more accurate than a standard method, yielding an average root mean squared target registration error of 1.6 mm. We conclude that self-calibrating registration results in significant improvements in registration accuracy for CAOS applications over conventional approaches where calibration parameters of the 3D-US system remain fixed to values determined using a preoperative phantom-based calibration.     

基于FPDE-SIFT的声呐干涉图像配准方法

图像配准是声呐进行高精度干涉测量的保障,该文针对水下目标的声呐图像配准,提出了一种基于4阶偏微分方程尺度不变特征变换的声呐干涉图像配准方法。该方法聚焦声呐图像配准的难点,首先基于4阶偏微分方程构建尺度空间,在保持图像细节的前提下滤除噪声,提高特征提取的准确度;对于残余噪声造成的特征点误检,借助特征点的相位一致性信息加以筛选,精简特征点样本集;最后对特征点匹配策略进行优化,提出改进的快速样本一致性匹配策略剔除特征点的误匹配。算法增加了匹配点对的数量,提高了匹配点对的准确度,实现了声呐干涉图像的精确配准。水池实验和外场试验表明,该文所提出的算法相较现有算法对声呐图像有着更好的适用性,配准后的均方根误差与留一法均方根均小于1像素,达到了亚像素配准精度。     

非刚体图像配准的变形场拓扑约束研究

利用正则化方法约束非线性变形场是非刚体图像配准领域的一个重要研究方向。为得到具有拓扑保持能力的非线性变形场,本文在分析粘流体配准和扩散模型配准算法的实现原理基础上,提出一种基于弹簧约束的变形场拓扑保持方法。该方法在可变形图像上附加不规则网格,通过保持网格结点间的连接关系不变达到控制图像变形的目的。将本文算法应用在不同人脑磁共振图像配准和脑内核结构分割中,结果表明,本文方法具有保持变形场拓扑不变的能力,且能够给出更为准确的分割结果。      

基于图像配准的指静脉身份认证技术

手指静脉在采集时手指旋转和姿态改变导致静脉形变和图像阴影,影响手指静脉身份验证的准确性。因此,文中提出一种基于图像配准的指静脉认证方法,通过少量稳定特征点配准认证图像,并提取两幅配准图像的纹理特征进行身份验证。该方法首先选择邻域清晰度高的静脉叉点作为特征点,然后对特征点进行匹配,并将两幅图像配准,最后根据纹理特征计算两幅配准图像的相似度。所提方法主要克服了基于特征点匹配的方法中点对数量少的问题,并解决了静脉形变的问题。实验结果证明,该方法较模板匹配和特征点匹配方法具有良好的性能。     

Evaluating similarity measures for brain image registration

Evaluation of similarity measures for image registration is a challenging problem due to its complex interaction with the underlying optimization, regularization, image type and modality. We propose a single performance metric, named robustness, as part of a new evaluation method which quantifies the effectiveness of similarity measures for brain image registration while eliminating the effects of the other parts of the registration process. We show empirically that similarity measures with higher robustness are more effective in registering degraded images and are also more successful in performing intermodal image registration. Further, we introduce a new similarity measure, called normalized spatial mutual information, for 3D brain image registration whose robustness is shown to be much higher than the existing ones. Consequently, it tolerates greater image degradation and provides more consistent outcomes for intermodal brain image registration.     

Bias minimizing filter design for gradient-based image registration

Gradient-based image registration techniques represent a very popular class of approaches to registering pairs or sets of images. As the name suggests, these methods rely on image gradients to perform the task of registration. Very often, little attention is paid to the filters used to estimate image gradients. In this paper, we explore the relationship between such gradient filters and their effect on overall estimation performance in registering translated images. We propose a methodology for designing filters based on image content that minimize the estimator bias inherent to gradient-based image registration. We show that minimizing such bias improves the overall estimator performance in terms of mean square error (MSE) for high signal-to-noise ratio (SNR) scenarios. Finally, we propose a technique for designing such optimal gradient filters in the context of iterative multiscale image registration and verify their further improved performance.