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针对海量声纳图像多帧配准引起的误差累积问题,该文提出了一种基于三维声纳图像的水底全景拼接与绘制方法。首先通过相邻两帧图像最近邻点迭代,并结合GPS、姿态仪信息和X84控制点剔除方法,减少非重叠区域控制点对的存在以提高配准精度,实现相邻帧图像精确配准。然后采用四元组参数对所有精确配准矩阵进行拟牛顿最优化处理,获取所有声纳帧之间最佳配准关系,减少多帧配准引起的累积误差,实现三维图像高精度全局配准。湖试与海试实验结果表明,该方法有效地减少了海量声纳图像多帧配准引起的累积误差,实现了三维声纳图像水底全景高精度拼接与绘制,达到了预期目标。 相似文献
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基于IKONOS-2全色与多光谱图像和Cartosat-1全色与CBERS-02多光谱图像,通过模拟的和真实的配准误差,采用IHS融合和Brovey融合方法,本文讨论了配准误差对融合结果的影响。实验表明,配准误差对融合质量的影响较大,在遥感图像融合处理中,配准误差越小越好;对于保持光谱信息的融合,当配准误差增加时,两种方法产生的光谱变形都迅速增加,此后光谱变形程度随配准误差的变大继续缓慢增强;对于保持空间信息的融合,配准误差的增大,导致两种方法融合结果的清晰度都下降,但下降速度略有不同。 相似文献
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面向室内弱纹理三维重建需求，本文以RGB-D摄影测量技术获取室内点云为基础，提出了四元组标靶辅助的点云配准方法。该方法首先通过阈值筛选大曲率点，自动识别邻接点云中的辅助标靶，然后采用随机采样一致性表达方法，拟合标靶参数及其中心坐标，并根据拟合参数匹配同名标靶中心，通过刚性转换完成邻接点云粗配准。在此基础上，迭代估算邻接点云间的重叠区域，优化点云间的配准参数，从而实现点云精配准。利用Kinect相机获取两类室内场景各12站点云对本文方法进行测试，试验结果表明，配准后的多站点云间距最大均方根误差优于一个采样间隔，证明了该方法在弱纹理室内点云配准中的可靠性。 相似文献
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常规的InSAR影像配准方法主要是根据相干系数最大的原则进行配准,在短基线和高相干区域可以取得很好的效果。但该方法未考虑到地形因素的影响,在地形起伏较大的区域容易导致误匹配。为避免该问题,通过考虑地形因素进行InSAR影像配准来提高配准的精度,对地形因素在InSAR影像配准时的影响进行研究,通过比较分析常规配准方法与考虑地形因素配准方法的结果,表明利用地形信息辅助配准能够部分提高配准的精度,尤其是在方位向和失相干区域。 相似文献
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光束法区域网平差的地面激光扫描多站点云自动定向方法 总被引：1，自引：1，他引：0
地面激光扫描（Terrestrial Laser Scanning）用于工程测量,需通过有限视场、不同视角、不同空间分辨率的多站扫描,多扫描站的自由坐标系下的点云需要纳入到工程指定坐标系。我们以摄影测量的空中三角测量理论为基础,提出基于光束法区域网平差的地面多站点云自动整体配准理论,包括配准标志的布设测量、全区域网的构建、光书法区域网平差。实验表明,配准后点云平面点位中误差为20.8mm,高程中误差为9.7mm,证明了配准的高精度和可靠性。 相似文献
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大孔径静态干涉成像光谱仪获取的图像是叠加了干涉信息的二维图像,点干涉图的获取需要经过全视场的推扫,平台姿态误差会导致提取的干涉图存在误差,进而影响复原光谱准确性,因此,图像配准是干涉图提取的关键。由于图像受到干涉调制作用,导致传统的图像配准方法在应用于LASIS图像配准时配准精度有限。为了减弱干涉条纹对图像配准的影响,本文提出一种利用条纹模板消除LASIS图像条纹,再结合图像频域配准的方法实现LASIS图像的高精度配准。配准结果表明,本方法很好地消除了干涉条纹对配准精度的影响,配准精度可以达到0.029 4个像素,相对于已开展的LASIS图像配准方法,沿轨方向配准精度提高了45.48%,跨轨方向配准精度提高了52.22%,图像旋转精度提高了39.13%。 相似文献
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ZHANG Jianqing ZHANG Zuxun FANG Zhen FAN Hong ZHANG Jianqing Professor National Laboratory 《地球空间信息科学学报》1999,2(1):16-20
Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images. 相似文献
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Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images. Some experimental results show that the method introduced in this paper is effective. 相似文献
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遥感影像CVA变化检测的CUDA并行算法设计 总被引：1，自引：1，他引：0
随着遥感影像数据量以及复杂程度的日益增加,遥感图像的快速处理成为实际应用过程中亟需解决的问题。为了实现遥感影像的实时变化检测,针对基于变化矢量分析CVA的变化检测算法,设计了一种基于统一计算设备构架CUDA的并行处理模型。首先利用地理空间数据提取库GDAL实现大数据量遥感影像的分块读取、操作和保存;其次将基于变化矢量分析的变化检测过程分为变化强度检测、映射表构建和变化方向检测,并借助CUDA C将变化矢量分析算法的3个步骤嵌入到CPU和GPU组成的异构平台上进行实验;最后利用该模型对不同数据量的遥感影像进行CVA变化检测并作对比分析。实验结果表明:与CPU串行相比,基于GPU/CUDA的遥感影像CVA的变化检测速度提高了10倍左右;在一定程度上,达到了实时变化检测的效果。 相似文献
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面向对象的多特征分级CVA遥感影像变化检测 总被引：1，自引：0，他引：1
变化矢量分析CVA方法在中低分辨率遥感影像变化检测中已得到广泛应用,但由于高分辨率遥感影像存在不同地物尺度差异大、不同类别地物光谱相互重叠的问题,因此对于高分影像的变化检测具有局限性。为提高高分影像变化检测精度,提出了一种面向对象的多特征分级CVA变化检测方法,首先,利用基于区域邻接图的影像分割方法分别对两时相遥感影像进行多尺度分割,提取分割图斑的光谱、纹理和形状特征;然后,在各级尺度下,分别运用随机森林方法进行特征选择,计算CVA变化强度图;最后,根据信息熵对多级变化强度图进行自适应融合,利用Otsu阈值法检测变化区域,并与仅考虑光谱特征的分级CVA变化检测方法、像元级多特征CVA变化检测方法以及仅考虑光谱特征的像元级CVA变化检测方法进行比较分析。实验表明:与比较方法相比,本文方法的变化检测精度较高,误检率和漏检率较低。 相似文献
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Automatic change detection of geo-spatial data from imagery 总被引：1，自引：0，他引：1
LIDeren SUIHaigang XIAOPing 《地球空间信息科学学报》2003,6(3):1-7
The problems and diffi-culty of current change detection tech-niques are presented. Then, according to whether image registration is done before change detection algorithms,the authors classify the change detec-tion into two categories:the change de-tection after image registration and the change detection simultaneous with image registration. For the former,four topics including the change detec-tion between new image and old im-age, the change detection between new image and old map, the change detec-tion between new image/old image and old map, and the change detection be-tween new multi-source images and old map/image are introduced. For the latter, three categories, i. e. the change detection between old DEM,DOM and new non-rectification image,the change detection between old DLG, DRG and new non-rectification image, and the 3D change detection between old 4D products and new multi-overlapped photos, are dis-cussed. 相似文献
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在遥感影像结合矢量数据先验信息的变化检测中,需要从分割后的影像对象中抽取一定数量、具有相同类别属性的样本,其中不可避免地抽到类别属性不一致的样本,如何剔除这些样本是抽样过程中必须解决的重点问题,在目前已有的方法中,一般是通过人工目视判别完成的。样本的自动提取是实现自动变化检测的关键环节,本文提出一种变化检测样本自动抽样方法,主要包括样本的空间布设和异常样本自动检测两个环节。该方法首先利用矢量数据提取抽样图层,用抽样图层分割遥感影像,获取影像对象。其次是根据抽样区域范围、影像对象分布特征和地形特征布设变化检测样本。然后根据样本的先验类别属性构建特征空间向量,计算样本在特征空间的局部可达密度,由局部可达密度计算样本的异常度指数,并根据特征空间密度异常指数剔除异常样本,完成变化检测样本自动提取。最后以耕地、林地和居民地为例进行了抽样试验。结果表明,邻域参数k按样本布设总数的1/5—1/3取值、异常度阈值设定为80%时,可以实现异常样本0漏检率,能够准确、高效实现变化检测样本的自动提取。 相似文献
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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. 相似文献
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针对目前高分辨率遥感影像变化检测算法对于光谱变化过敏感问题,本文提出了一种基于超像素分割与条件随机场（CRF）的遥感影像变化检测算法。首先采用空间约束的t混合模型驱动的分割模型,获得同质性超像素块,实现良好的边界附着性和亮度均匀性。然后计算分割得到的双时相影像块之间的特征差异性,获取变化幅度图像。最后利用模糊聚类算法（FCM）对变化幅度图像进行聚类,得到隶属度图像作为CRF一阶势,并利用光谱-空间相似度约束的函数构建CRF二阶势。试验结果表明,与现有方法相比,该方法检测精度可提高5%,错检率和漏检率可降低3%,能较好地应对输入图像的光谱变化,并保持变化检测结果的边缘细节。 相似文献