Curvedness feature constrained map matching for low-frequency probe vehicle data

Map matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments.     

基于改进K-SVD字典学习方法的地震数据去噪

为实现更好的地震数据去噪技术,笔者引入一种新的算法:快速迭代收缩阀值法(FISTA),通过FISTA和K-奇异值分解(K-SVD)不断迭代更新K-SVD字典,利用更新得到的K-SVD字典对地震数据进行稀疏表示,去除稀疏系数中较小的数值,使数据中的随机噪声得到压制。对层状模型合成地震记录,Marmousi模型合成地震记录以及实际地震数据进行对比实验,得出FISTA算法较OMP算法能更好地提高地震数据的信噪比,同时有效地保护了反射信号。     

Geometric-constrained multi-view image matching method based on semi-global optimization

Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models, this paper presents a geometric-constrained multi-view image matching method, based on an energy minimization framework. By employing a geometrical constraint, the cost value of the energy function was calculated from multiple images, and the cost value was aggregated in an image space using a semi-global optimization approach. A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculating cost values. It is based on the known interior orientation parameters, exterior orientation parameters, and a given elevation value. For an efficient and reliable processing of multiple remote sensing images, the proposed matching method was performed via a coarse-to-fine strategy through image pyramid. Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method. Results reveal that the multi-view image matching can improve matching reliability. Moreover, the experimental results show that the proposed method performs better than traditional methods.     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

低剖面紧凑的圆盘加载单极子天线

本文介绍了一种低剖面小型化圆盘加载单极子天线.通过盘加载的方式,在中心频率5.8 GHz处,首先将单极子天线的剖面从大约12.9 mm（0.25λ₀）降低到2 mm（0.039λ₀）,实现了低剖面的特性.加载在单极子天线上方的圆盘,尺寸大概为21 mm（0.41λ₀）,因此天线具有结构紧凑的特点.接着,为了使天线具有良好的匹配,在加载圆盘四周加入4个接地通孔,并且在馈电端口加入集总电感.最终,天线在中心工作频率5.8 GHz处实现了反射系数-15 dB的匹配性能.     

对吐哈盆地相邻两幅遥感图像的辐射匹配

在ERDAS软件的支持下,对吐哈盆地相邻的两幅ETM遥感图像采用所介绍的直方图加减均值差和方差均值2种方法进行辐射匹配,并得出基于这2种方法的几点结论用于提高辐射匹配程度。同时为了降低匹配时存在的人为误差,提出了一种新的多样本均值的辐射匹配方法。最后通过对3种匹配方法的结果进行定量性检验,证明多样本均值的辐射匹配方法能达到较好的匹配效果,其匹配结果要优于前2种方法,具有可行性。笔者的目的在于:经过对不同方法的运用、对比及验证,提出了一种更为优越的辐射匹配方法———多样本均值法,以用于对吐哈盆地整体的辐射匹配。较好的匹配效果有助于对盆地内一些相关指数的准确评价和其砂岩型铀矿的分析研究。     

IKONOS影像城市植被信息提取前的阴影校正

在高分辨率卫星影像的预处理上,阴影的提取与校正是一个难题,本文根据实际需要,采取不同的方法对建筑物阴影和山体阴影进行提取,并采用不同的校正方法进行校正。对城区建筑物阴影,采用基于影像融合的IKONOS影像阴影自动提取方法对阴影进行提取,再采用郎伯模型进行校正;对山体阴影,直接采用监督分类的方法对山体阴影进行提取,再采用灰度线性匹配的方法进行校正。实验表明,分开提取与校正的策略能取得较为理想的效果。     

控制点影像库在遥感影像几何纠正中的应用

影像几何纠正是遥感图像处理的重要技术。为了克服现有方法在实际应用中的不足,提出利用控制点影像库进行影像自动匹配,然后进行几何变化的方式。这种方法简单有效,快速稳定,匹配得到的同名点数量足够且分布均匀,并通过引入最小二乘匹配使配准达到子像素精度,提高几何纠正的速度与可靠性。     

Artificial ground motion compatible with specified ground shaking peaks and target response spectrum

1 Introduction Time history analysis is usually needed not only for the seismic design of important structures, such as nuclear plants, large concrete dams and super high-rise buildings, but also for seismic performance based design. Performance-based design requires tha knowledge of the entire process of nonlinear seismic responses of structures be obtained, which depends on the ground motion time history as input for this analysis. However, because the observed strong-motion recordings are l…