无人艇载高清多光谱影像配准

针对无人艇载高清多光谱影像特性,提出了集成单应性矩阵内配准与置平薄板样条外配准的多光谱影像整体配准方案:(1)傅里叶-梅林变换引导下的多光谱影像相关匹配自动生成连接点;(2)单应性矩阵最小二乘联合估计实现多波段影像相互配准(内配准);(3)构建“置平”薄板样条变换模型实现多光谱影像序列无地理坐标配准(外配准),最后对无人飞艇搭载多CCD相机低空拍摄的高清多光谱影像进行配准验证,取得了约1个像元误差的内配准精度以及较好的外配准视觉质量。     

基于几何配准的多模式SAR影像配准及其误差分析

星载合成孔径雷达影像干涉处理时所需方位向配准精度因成像模式的差异而有所不同,目前在精密轨道条件下以几何配准为基础辅以影像信息的配准方案因其严格的理论模型和较高的精度成为干涉处理的首选。本文以TerraSAR-X影像为例,论证了不同成像模式影像所需的配准精度和卫星轨道精度,并通过理论分析和试验证明了精密轨道条件下,利用几何配准即可满足TerraSAR-X等卫星的条带模式影像干涉处理的需要;聚束模式影像需要在几何配准的基础上利用影像相干性或谱分集进一步优化配准结果。鉴于增强谱分集偏移量估计精度最高,本文进一步利用增强谱分集对比分析了不同轨道不同DEM条件下的几何配准误差。研究结果表明:卫星轨道切向误差是几何配准的主要误差源,目前常用3种DEM几何配准差异远小于0.001个像素,均可满足Sentinel-1影像干涉配准的需要。     

车载激光雷达点云与全景影像的自动配准

针对激光雷达点云数据缺乏纹理信息的问题,该文提出一种基于互信息的车载激光雷达点云与全景影像配准方法。该方法使用统一的球面全景成像模型,引入互信息作为相似性测度,将车载激光雷达点云生成的深度图与信息提取后的全景影像进行配准,实现配准参数的自动、高精度解算。同时,对车载激光雷达点云与全景影像配准的精度进行评定与分析。实验结果表明,车载点云与全景影像的配准方案是可行的,具有较高的配准精度。     

一种带几何约束的大幅面遥感影像自动快速配准方法

针对大幅面遥感影像的配准需求,提出了一种带有几何约束的大幅面遥感影像快速自动配准方法。该方法首先结合遥感影像空间地理信息构建影像粗配准模型;然后在粗配准模型的约束下,通过对大幅面遥感影像进行规则格网划分,结合改进的SIFT算法和并行策略实现影像精配准。实验结果表明,本文方法能提高影像的配准效率,且配准精度较高,适用于大幅面遥感影像之间的配准。     

一种考虑云层影响的遥感影像波段配准方案

对高光谱遥感影像中运动云层对波段配准的影响进行了分析,结合灰度拉伸和形态学空间运算提出了一种新的在影像有云条件下高光谱影像多波段配准算法。结合算法设计了较为稳定的多波段配准方案;利用Python编程开发实现了原型系统,并将原型系统在珠海一号高光谱卫星数据上进行了实验。结果表明,系统具有良好的效果。     

宽河道线性基元无人机遥感影像镶嵌配准方法

无人机遥感影像由于数量多、单张覆盖空间范围小、几何变形大以及河道内缺少配准控制点等特点,传统的基于特征点的影像镶嵌配准方法在宽河道无人机遥感影像镶嵌配准应用中受到限制.论文根据河道内线性地物丰富的特点,提出基于线性特征的无人机遥感影像镶嵌配准方法.该方法以线性地物边缘直线段为配准基元,通过MIHT算法估算待配准影像与基准影像的变换模型参数.试验结果表明,该方法很好地满足了宽河道无人机遥感影像镶嵌配准的要求,为沿河地质灾害调查提供了影像质量保证.     

基于投影变换与Fourier-Mellin变换结合的图像配准

傅里叶梅林变换是比较优秀的图像配准方法之一,但此方法不适用于倾斜影像。针对此问题提出了一种基于投影变换与Fourier-Mellin变换相结合的图像配准方法。该方法首先根据投影构像方程计算标准图像与待配准图像之间的投影变换矩阵;然后根据投影变换矩阵对待配准影像做倾斜校正;最后对纠正后的待配准影像与标准影像进行Fourier-Mellin变换,得到准确的配准参数。实验结果表明,该方法使得Fourier-Mellin变换的图像配准方法不仅适用于相对平行影像对之间配准,也适用于非相对平行影像对配准,且在直观与客观上都比传统傅里叶梅林变换配准结果好。     

顾及灰度和梯度信息的多模态影像配准算法

基于特征匹配的多模态影像配准方法无法达到像素级配准精度要求。本文研究了一种顾及灰度和梯度信息的多模态影像配准算法。基于马尔科夫随机场（MRF）的非参数化配准模型充分利用多模态影像的图像信息进行相似性测量,同时考虑了灰度及梯度统计信息,求解方法上对值空间进行离散化,提高收敛速度。通过3组多模态影像的配准试验,验证了该算法的可行性。试验表明：本文算法的配准效果优于基于人工刺点的多项式模型配准和只考虑灰度信息的多模态影像配准;与此同时,该算法对于较大形变的影像配准也具有一定的适用性。在空间精度方面,平均配准误差小于1个像素,最大配准误差小于2个像素。     

遥感影像深度学习配准方法综述

遥感影像配准是指通过几何变换使两景或多景影像空间位置对齐的过程，是影像融合、变化检测、农业监测等应用的重要预处理步骤。近年来，深度学习引起了人们的广泛关注，并在遥感影像配准中成功应用。本文在简要介绍传统遥感影像配准方法的基础上，重点分析了深度学习在基于区域的配准方法、基于特征的配准方法两方面取得的重要进展，分享了用于遥感影像配准的公开数据集，并总结了深度学习在遥感影像配准中的机遇与挑战。     

基于互信息的LIDAR与光学影像配准方法

本章论述了基于互信息的是单幅影像与LIDAR数据的配准方法。本方案是基于两幅图像灰度值的配准方法,即利用LIDAR数据生成的强度图像,实现与航空影像的配准。虽然两种传感器得到图像的灰度值有较大差异,且不成线性关系,通过引入互信息,可以将穷举的搜索问题变成基于相似性测度函数的优化问题。实验表明互信息准则对于航空影像与LiDAR数据之间是有效的。     

星载SAR复数图像的配准

星载干涉雷达测量已经被证实用于生成大范围（乃至全球）的DEM或监测地表位移具有广阔的应用前景，其中SAR复数图像序列的精确配准是保证生成有效干涉相位图的重要环节之一。本文分析了卫星InSAR系统中所涉及的几种坐标系统变换问题，并提出了基于此的几何配准方案，即基于卫星轨道状态矢量、SAR成像几何进行配准的算法；同时，还设计了基于几何配准结果和能量影像相关技术的配准算法，最后使用ERS－1／2、JERS－1和RADARSAT卫星获取的3种SAR复数图像对分别做几何配准和相关配准试验和比较，并对卫星SAR图像质量和轨道数据质量作了定性分析。     

一种高精度的干涉雷达复数影像配准方法

在总结现有算法的基础上,提出了基于相干系数、Harris特征点、小波金字塔及TIN三角微分纠正技术的单视复数雷达图像的配准流程。通过ERS 1/2的实验表明,提高了配准的精度和效率,特别是保证了在没有精确轨道甚至没有轨道参数的情况下也能获得很高的配准精度,计算正确的干涉相位图。在重采样过程中采用的三角联网策略,进一步使匹配点的局部拟合误差得到有效控制,得到配准精度更高的复图像对。在CPU 3.06GHz计算机上,43 s内完成5000像素×1000像素的主辅图像的配准,平均相干系数为0.719855。     

几种卫星合成孔径雷达影像配准算法的比较研究

复数影像配准是卫星合成孔径雷达干涉数据处理中的重要步骤之一,影像配准精度将直接影响干涉条纹图的质量和由此提取的高程或位移结果的精度。文中利用上海地区ERS-1/2影像数据进行实验,对比分析了三种方法的配准质量和运算效率,试验结果表明:在配准质量方面,相干系数法最高,相位差影像平均波动函数法次之;而在信噪比较低的情况下,最大频谱法的配准质量优于相位差影像平均波动函数法。在运算效率方面,相位差影像平均波动函数法的运算效率最高。     

基于目标关联的多源卫星遥感图像兵营融合检测方法

目前的目标融合检测方法大都是基于多源遥感图像配准的,然而在实际的应用中,成像机理不同的多源遥感图像的精校正和图像间的配准是十分复杂的,难以确保其配准精度.为此,本文提出了一种基于目标关联的多源卫星遥感图像的兵营融合检测方法.该方法不对图像进行配准,而是根据单源图像的目标自动检测结果,利用图像的大地坐标信息,截取包含目标的同一地区的局部遥感图像,再分别提取多源遥感图像目标的特征,并根据其中冗余的特征,对提取的目标区域建立关联,再由关联检验确保特征关联的正确性,最后对目标特征进行融合决策,得到目标融合检测结果.实验结果表明,该方法能有效地利用多源遥感图像的信息,降低遥感图像目标检测的误判率,提高目标特征的准确度.     

一种改进的星载干涉SAR复图像最大频谱配准算法

最大频谱法常用于星载干涉SAR复图像配准,但该方法计算量较大且易受噪声影响。本文提出一种改进的最大频谱配准算法。该方法利用chirp-z变换替换补零FFT变换,以相对较少的运算量达到较高的频谱峰值计算精度;通过设定合理的判决门限,判定控制点偏移量估计结果的可靠性,以便对位于不同区域的控制点自适应选取子图像截取窗口的长度,达到控制运算量的目的。利用该算法分别对来自ASAR和ERS-1/2的两对复图像进行验证,实验结果表明该算法可以有效实现配准,且比同条件下利用常规最大频谱算法得到的结果更加可靠。     

RESOURCESAT-1 LISS-4 MX bands onground co-registration by in-flight calibration and attitude refinement

RESOURCESAT-1 satellite was launched in October of 2003. Since then it has been consistently providing high quality 5 m monochromatic and multispectral images of same resolution. LISS-4 MX sensor has complex acquisition geometry. It operates in three spectral bands imaged by 3 CCD arrays, which are separated by a finite time in imaging along the satellite track direction. Individual band data is acquired at different times while the satellite is driven by a pre-determined yaw profile. In addition, the odd–even pixels are too shifted by a small fixed delay in time. A unique challenge in LISS-4 MX Level-2 data processing sub-system is to autonomously rectify and additionally co-register the three bands data because of the influence of orbit and attitude in the time gap in the imaging sequence. In this paper, authors bring out details of in-flight calibration arrived for LISS-4 MX sensor. It addresses parameterization of co-registration problem by doing sensitivity analysis of the geometric model parameters to achieve co-registration among all bands. This approach can also be used for other sensor system having similar imaging geometry to achieve improved image co-registration among bands.     

Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images

The accuracy of topographic correction of Landsat data based on a Digital Surface Model (DSM) depends on the quality, scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image. A physics-based bidirectional reflectance distribution function (BRDF) and atmospheric correction model in conjunction with a 1-second DSM was used to conduct the analysis in this paper. The results show that for the examples used from Australia, the 1-second DSM, can provide an effective product for this task. However, it was found that some remaining artefacts in the DSM data, originally due to radar shadow, can still cause significant local errors in the correction. Where they occur, false shadows and over-corrected surface reflectance factors can be observed. More generally, accurate co-registration between satellite images and DSM data was found to be critical for effective correction. Mis-registration by one or two pixels could lead to large errors of retrieved surface reflectance factors in gully and ridge areas. Using low-resolution DSM data in conjunction with high-resolution satellite images will also fail to correct significant terrain components where they occur at the finer scales of the satellite images. DSM resolution appropriate to the resolution of satellite image and the roughness of the terrain is needed for effective results, and the rougher the terrain, the more critical will be the accurate registration.     

基于矩阵相似度的InSAR图像配准方法研究

图像配准是干涉合成孔径雷达成像的关键技术之一,相关系数是InSAR图像配准的重要指标。本文在分析研究常见相似性度量的基础上,研究了相关系数与矩阵相似度度量之间的关系,研究发现,矩阵相似度度量可涵盖多种图像配准数值指标,且其表达形式精炼,几何意义明确,便于把握图像配准的机理。此外,本文提出了一种加权矩阵相似度度量,并通过仿真数据进行了验证。     

月球轨道器影像摄影测量制图进展及应用

本文对月球轨道器影像摄影测量制图技术进展、产品及应用进行综述,着重介绍了月球轨道器影像几何模型构建与精化、月球遥感影像与数字高程模型配准、多重覆盖影像择优方法及大区域制图等新技术的进展与应用。结合我国的嫦娥工程任务,阐述了月球轨道器摄影测量制图在工程及科学研究中的应用。最后对月球轨道器摄影测量制图技术未来的发展进行展望和探讨。     

3D change detection at street level using mobile laser scanning point clouds and terrestrial images

Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.