基于SIFT和NCC的多源遥感影像配准方法

针对多源遥感影像的配准,提出了一种结合SIFT算法和归一化互相关（NCC）匹配算法的配准方法。该方法采用SIFT算法提取特征点并进行匹配得到一定数量的特征点对后,利用SIFT特征点的尺度和方向信息对NCC进行改进,进一步从未能匹配的特征点中获取匹配点对,经粗差滤除后得到有效的匹配特征点对,随之进行影像配准。方法结合了SIFT算法和NCC算法的优点,解决了多源遥感影像因辐射差异和几何差异造成的难以正确配准的问题。实验结果表明,算法具有较强的鲁棒性,并取得了较好的配准精度。     

基于SIFT点特征和Canny边缘特征匹配的多源遥感影像配准研究

针对多源遥感影像的配准,提出了一种结合SIFT点特征和Canny边缘特征匹配的配准算法。该算法首先采用SIF7算法提取点特征并进行影像粗配准,在获得初始仿射变换参数后,采用Canny算法提取边缘特征,并采用成本函数法进行边缘点匹配,经粗差滤除后得到有效的匹配特征点对,随之进行影像精配准。该算法结合了SIFT、算法和Canny算法的优点,解决了多源遥感影像因辐射差异和几何差异造成的难以正确配准的问题。实验结果表明,算法具有较强的鲁棒性,并取得了较好的配准精度。     

一种利用点特征和互信息的多源遥感影像配准方法

针对多源遥感影像间几何形变和辐射差异所引起的配准困难问题,该文提出一种利用点特征和互信息的影像配准方法。该方法首先利用Harris算子,采用分块的提取策略,在参考影像上检测出大量、分布均匀的特征点,然后以互信息作为相似性,并采用双向匹配的策略在输入影像上进行同名点识别,同时进行粗差剔除,最后根据获取的同名点建立不规则三角网,对输入影像进行逐三角网纠正,最终实现影像的配准。通过选择两种不同类型的多源遥感影像进行试验,结果表明该方法具有较强的适应性并且配准精度较高。     

利用稠密结构特征的航空影像与LiDAR数据配准北大核心CSCD

针对航空影像与激光雷达点云数据间存在显著的几何畸变和辐射差异导致难以精确配准的问题,提出了一种基于几何约束和稠密结构特征的自动配准方法。该方法包括粗配准和精配准两个阶段。粗配准通过基于分块FAST算子的特征点提取和局部几何校正两个步骤,消除影像间明显的尺度和旋转差异。在精配准阶段,首先构建了一个结合一阶和二阶梯度信息的新描述符(second-and first-order channel features of orientated gradients,S-CFOG)来提取影像间稠密的结构特征,然后在频率域采用三维相位相关作为相似性度量进行同名点匹配。最后,利用同名点对外方位元素进行精化,实现对这两类数据的精配准。通过两组不同覆盖场景的数据进行实验,结果表明,该方法可达到1~2个像素的配准精度。     

基于VoxelMorph的可见光和红外遥感影像配准

由于可见光和红外的成像机理、成像波段不同,获取的遥感影像之间存在复杂的非线性辐射畸变,传统的配准方法难以实现两者的高精度配准。本文提出一种基于VoxelMorph的可见光和红外遥感影像配准方法,利用卷积神经网络对可见光和红外异源图像进行分步的精细化形变场计算,从而实现快速高精度配准。将可见光图像作为参考图像,利用U-Net网络计算待配准红外图像和参考（可见光）图像的形变场,实现全局对齐的仿射变换,然后通过空间转换网络进一步实现更高自由度变形。采用WHU-OPT-SAR数据集的实验结果表明,与基于尺度不变特征变换（SIFT）算法的传统配准方法相比,本文提出的基于VoxelMorph配准方法可以获得更好的配准效果,验证了基于VoxelMorph的配准方法在多源遥感影像领域的有效性。     

基于SIFT与互信息筛选优化的遥感影像配准

针对多源多尺度影像配准中存在误匹配率较高和配准精度较低的问题,提出了一种基于（Scale-Invariant Feature Transform SIFT）与互信息筛选优化的影像配准算法。首先,采用SIFT算法进行特征点提取,通过快速最近邻逼近搜索（Fast Approximate Nearest Neighbors Search Library,FLANN）算法完成待配准影像的粗匹配,其次,在初始匹配点周围建立4×4邻域,计算匹配点之间的互信息值,对互信息值较小的匹配点进行剔除,寻求筛选优化后的最优变换矩阵,最后输出与基准影像互信息值最大的配准后影像作为最佳配准结果。实验结果表明：该方法与SIFT算法相比可以有效地剔除误匹配点并提高了配准精度。该方法可以应用于多源多尺度遥感影像配准,能够有效地提高配准精度。     

基于边缘保护多尺度空间的医学图像配准方法

从尺度空间滤波的角度分析传统多分辨率配准方法存在局限性的原因。为提高配准的精度和速度,更好地避免局部极值,提出基于边缘保护多尺度空间配准的方法。这种多尺度空间基于非线性扩散模型,可以为基于互信息的配准提供丰富的空间位置信息。同时为实现全自动配准,提出自动获取非线性扩散模型中平滑参数λ的方法。实验结果表明,文中方法用于三维医学图像配准时,优于传统的多分辨率配准方法,配准结果获得更高的精度,需要较少的迭代次数,并且在传统方法发生误配时,文中方法仍可准确配准,具有较好的鲁棒性。     

基于改进尺度不变特征的图像局域几何配准研究

针对图像配准容易产生误配准、漏配准的问题,提出了基于改进尺度不变特征的图像局域几何配准。该方法改进了尺度不变特征,通过构建边缘尺度空间设计了尺度不变边缘特征变换,融合了尺度不变特征点和尺度不变边缘。以尺度不变特征为基础,搜寻图像间的局域图像变换,实现图像局域几何配准。实验表明,SIFT特征点和边缘信息互补能够提供更多的配准信息并减少错误配准;该方法对尺度、噪声、形变、光照等不敏感,能够配准移动目标,真实地反映图像的配准状况。     

基于自适应尺度的遥感影像渐进配准

图像配准是遥感图像处理中的基本问题.本文针对多源多时相遥感影像的特点,提出了一种基于自适应尺度的渐进配准方法,在从粗到细的迭代配准过程中,可以通过上一次配准结果的几何定位误差来确定本次匹配的尺度,并按该尺度提取特征角点和特征邻域进行匹配,与常规金字塔渐进配准方法相比,减少了匹配次数,提高了配准效率.另外,特征提取和匹配过程中提出一种基于Harris-Laplace算法和相位相关算法的遥感影像配准算法,利用Harris-Laplace角点代替原始图像,能够综合区域和特征的优点,对亚像元偏移、旋转、尺度变化具有不变性,同时对对比度和灰度的变化不敏感,具有很强的抗噪性.在特征检测和匹配的过程中采用限定搜索区域、抽稀角点等多种优化策略来提高算法的性能.实验证明,算法具有很好的精度,对几何攻击具有很好的鲁棒性,该算法已经应用于CBERS-02B星3级数据的批量自动化生产,具有很好的应用效果.     

图像多尺度配准的小波域SURF算法

针对SURF描述子仅利用特征点的局部邻域信息而对于局部场景发生变化的图像容易产生误匹配的现象,提出了图像多尺度配准的小波域SURF算法。该方法对图像进行小波分解,利用低分辨率上得到的变换参数剔除高分辨率上的错误匹配,得到精确匹配点对,对图像进行配准。实验结果表明,该方法能有效地剔除误匹配点,提高图像配准的精度。     

A novel extended phase correlation algorithm based on Log-Gabor filtering for multimodal remote sensing image registration

Automatic registration of multimodal remote sensing images, which is a critical prerequisite in a range of applications (e.g. image fusion, image mosaic, and image analysis), continues to be a fundamental and challenging problem. In this paper, we propose a novel extended phase correlation algorithm based on Log-Gabor filtering (LGEPC) for the registration of images with nonlinear radiometric differences and geometric differences (e.g. rotation, scale, and translation). Our algorithm focuses on two problems that the traditional extended phase correlation algorithms cannot well handle: 1) significant nonlinear radiometric differences and 2) large-scale differences between image pairs. After an over-complete multi-scale atlas space of the original image is built based on the filtered magnitudes obtained by using Log-Gabor filters with different central frequencies, the phase correlation of the single scale images is extended by LGEPC to atlases phase correlation, which is conducive to solving the problem of large scale and rotation differences between the image pairs. Subsequently, LGEPC eliminates the interface of the significant nonlinear radiometric differences by superimposing multi-scale geometric structural spectra and carrying out the phase correlation module, so that the translation can be well determined. Our experiments on synthetic images demonstrated the rationality and effectiveness of LGEPC, and the experiments on a variety of multimodal images confirmed that LGEPC can ideally achieve pixel-wise registration accuracy for multimodal image pairs that conform to the similarity transformation model.     

An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images

On 6 September 2008, two optical satellites, HJ-1 A and B (HJ-1 A/B), were successfully launched from China. However, the system geometric correction products of the HJ-1 A/B charge-coupled device (HJ-1 images) have low geometric precision and need to be corrected. The HJ-1 images have a large aspect angle, a wide swath width, and a large image size. Furthermore, the local geometric distortions are too complex in one scene. Given these characteristics of HJ-1 images, geometric correction is still a challenging work. This article proposes an automatic geometric precision correction system (GPCS) based on the automatic registration between HJ-1 images and Landsat Thematic Mapper images. First, the coarse image matching method based on geometric-restricted scale-invariant feature transform (SIFT) is used to determine the coarse global transformation between the HJ-1 image and the reference image. Second, inspired by the hierarchical method of non-rigid registration for medical images, a hierarchical image matching approach is proposed based on the combination of SIFT feature points and template matching. This approach decomposes a matching problem of a whole image into numerous matching problems of image blocks and can overcome the impact of local distortions in HJ-1 images. Hierarchical random sample consensus (RANSAC) based on digital elevation model (H-RANSAC) is used to remove incorrect control points. Third, an HJ-1 image is rectified using a triangulated irregular network. Finally, the automatic evaluation method based on automatic image matching between the corrected HJ-1 image and the reference image is adopted to evaluate the geometric precision. On the one hand, experiments on eight HJ-1 images demonstrate the efficiency and accuracy of the different steps of GPCS. On the other hand, experiments on 1000 HJ-1 images also demonstrated the robustness, accuracy, and suitability for batch processing.     

尺度及主方向改正的ORB特征匹配算法

针对二进制描述算法（Oriented fast and Rotated Brief，ORB）尺度性配准误差大，配准率低的问题，提出一种尺度和方向改进的ORB特征匹配算法。该算法以二进制描述算法ORB为基础，构建金字塔式尺度空间，改进尺度空间结构，简化尺度空间层数和采样图像数目，使提取特征点的过程更加效率，并采用Harris函数检测特征，消除边缘特征点的影响，提取具有尺度信息的特征点；然后采用梯度方向统计方法改进传统ORB算法中通过灰度质心法计算主方向的方式，优化求解主方向邻域范围，以提高图像特征主方向的准确性。实验结果表明，改进后的ORB算法在尺度和旋转配准方面性能有很大提高，并且配准的精度较传统ORB更高，更能满足复杂图像快速精确配准的要求。     

Wavelet-based image registration technique for high-resolution remote sensing images

Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints at different times or by different sensors. It is an important image processing procedure in remote sensing and has been studied by remote sensing image processing professionals for several decades. Nevertheless, it is still difficult to find an accurate, robust, and automatic image registration method, and most existing image registration methods are designed for a particular application. High-resolution remote sensing images have made it more convenient for professionals to study the Earth; however, they also create new challenges when traditional processing methods are used. In terms of image registration, a number of problems exist in the registration of high-resolution images: (1) the increased relief displacements, introduced by increasing the spatial resolution and lowering the altitude of the sensors, cause obvious geometric distortion in local areas where elevation variation exists; (2) precisely locating control points in high-resolution images is not as simple as in moderate-resolution images; (3) a large number of control points are required for a precise registration, which is a tedious and time-consuming process; and (4) high data volume often affects the processing speed in the image registration. Thus, the demand for an image registration approach that can reduce the above problems is growing. This study proposes a new image registration technique, which is based on the combination of feature-based matching (FBM) and area-based matching (ABM). A wavelet-based feature extraction technique and a normalized cross-correlation matching and relaxation-based image matching techniques are employed in this new method. Two pairs of data sets, one pair of IKONOS panchromatic images from different times and the other pair of images consisting of an IKONOS panchromatic image and a QuickBird multispectral image, are used to evaluate the proposed image registration algorithm. The experimental results show that the proposed algorithm can select sufficient control points semi-automatically to reduce the local distortions caused by local height variation, resulting in improved image registration results.     

基于误差扩散和均值漂移的DSA特征点选择

数字减影血管造影（DSA）技术在血管疾病的诊断和治疗中起着重要的作用。在DSA图像配准算法中,控制点的精度、数量以及它们在图像上分布的情况在很大程度上决定了几何校正和配准的精度。控制点通常采用规则的网格点,然而更加有效的方法是利用图像本身的特征提取控制点。提出了一种基于误差扩散（Error Diffusion）和均值漂移（Mean Shift）的DSA特征点选择算法。该算法引入误差扩散是为了利用图像本身的特征自适应地选择控制点。在边缘和纹理复杂区域取较多控制点（网格密集）,相反在平坦区域取较少控制点。引入均值漂移的目的是为了减少控制点的数量。实验结果表明,该算法可以自适应地把控制点安排到血管等关键图像特征上,并可以根据需要对控制点的数量进行优化。     

基于边缘几何特征的图像精确匹配方法

提出一组快速高精度计算切线斜率的五点公式，用以估计图像边缘曲线的角度特征，并利用角度直方图估计图像几何变换的旋转参数，实现具有大旋转差异图像间的粗匹配．在进行角度补偿后，利用灰度互相关判据搜索匹配点对，计算出几何变换参数，实现较高精度的旋转和平移校正，最后用松弛迭代法完成图像的精确匹配．与基于小波方向角特征的匹配方法相比，文中方法利用图像中主要的边缘信息实施匹配，具有较好的鲁捧性，可成功实现对各类具有较大相关程度图像间的精确匹配，对图形匹配也具有重要意义．     

结合边缘与灰度信息的SAR图像配准算法

同一场景下的合成孔径雷达(SAR)图像的灰度特性由于相关噪声的影响及成像条件不同可能存在很大差异,使得单纯基于边缘特征或灰度信息的方法难以胜任SAR图像配准工作。根据SAR图像的特点,提出一种典型地物边缘形状信息与局部灰度统计信息相结合的基于特征的图像配准方法,弥补了仅利用边缘特征或灰度信息的方法在SAR图像配准中的不足。给出了本方法用于Radarsat图像上的实验结果。