发扬夏坚白院士精神,倡导尊重知识、重视知识产权的行业之风

前不久,报章上出现了这样一条消息:国家信息产业部、国家版权局、商务部、财政部四部委联合下发了<关于计算机预装正版操作系统软件有关问题的通知>.根据通知精神,从现在开始没有预装正版操作系统的PC产品将无法上市销售.     

中国最美的古村落(十) 浙中奇绝 乌石古村

最吸引人的是这里的建筑,古民居外墙都由一小块一小块的乌石垒成,粗犷的外形与凝重的黑色相结合,结构精巧独特,给人以"鬼斧神工"的感觉,据说最早的乌石屋已有近三百年的历史.     

搜酷

强悍的摄像机作为索尼XR系列摄像机的开篇之作,HDRXR520E集众多项级配置于一身,采用了在低光照环境下有着出色表现的Exmor R CMOS感光元件,     

电脑族每天必喝的四杯茶

1.上午一杯绿茶绿茶中含强效的抗氧化剂以及维生素C,不但可以清除体内的自由基,还能分泌出对抗紧张压力的荷尔蒙。绿茶中所含的少量咖啡因可以刺激中枢神经,振奋精神。不过最好在白天饮用,以免影响睡眠。2.下午一杯菊花茶菊花有明目清肝的作用,有些人就干脆用菊花加上枸杞一起泡来喝,或     

国家测绘局跨世纪青年学术带头人李清泉教授

李清泉教授生于1965年1月,安徽天长人,中共党员,工学博士,武汉大学副校长,湖北省第八、九届政协委员,第二届"湖北省五四青年奖章"获得者,湖北省青年联合会第十届委员会副主席,武汉市青年联合会第十届委员会副主席,李清泉教授先后承担了教学、科研、产业和财务管理以及大量社会工作.     

同江:一曲赫哲族船歌

同江位于三江平原北端,黑龙江与松花江汇流处南岸的三角地带,是有百年历史的国际口岸城市,由佳木斯市代管。清末设立临江州,1905年辟为通商口岸,1914年以黑龙江、松花江在此合一而改名同江。东接抚远,南邻饶河,北与俄列宁斯科耶隔水相望,边境线长166公里,设有国家一类口岸。同江有八岔、街津口两个赫哲族乡,这是一个只有四千人口,却在漫     

中国最美的古村落(一)千古太极奇村——俞源探秘

俞源的经典在于村落结构的设计:村中有大小七个水塘组成北斗七星的格局,村内的28幢主要建筑则按照天上的28星宿排布。     

万维网GIS的若干关键技术及其实现

提出基于J2EE多层体系结构的WebGIS系统,能够平衡好服务器、应用服务器、客户端负载,极大地提高系统的响应速度;对海量的矢量数据与影像数据采用不同的压缩方式,在数据不失真的情况下尽量减少数据的传输量,减轻网络的负载;栅格数据与矢量数据的"无缝"叠加,更加形象清楚地发布空间数据;基于0racle Spatial关系数据库的WebGIS,使系统更加安全可靠.     

飞地:孤悬在外的领土

从明清之交,到解放初的三百多年间,在(四川)峨眉县普兴乡境内,曾有一个地名叫何沟(亦叫梅沟)的小山村,只有十几户人家,却直属嘉定府管辖,毗连的夹江,峨眉、洪雅、乐山四县,无权过问,是一个"世外桃源",当地人称它是"飞地"……梅沟成为一个四不管的角落.     

数字

1.26亿吨近日,黑龙江省煤田地质勘察队在鹤岗市萝北县江滨农场境内发现一处总面积为124.88平方公里,储量约1.26亿吨,煤层平     

Distance-limited filter for extracting ground points

The extraction of points on the bare earth from point clouds acquired by airborne laser scanning is one of the most important steps for the generation of digital terrain models (DTM). This process is called “filtering”. However, most of the current filters erode the bare earth in steep sloped landscapes and at discontinuities, and they retain low vegetation. Therefore, a new filtering method for extracting ground points based on a distance limit is proposed in this paper. The angle criterion is used to assure the robustness of the algorithm. The experimental results show that the proposed filtering method can effectively derive the ground points from point clouds in complex urban areas.     

基于距离限制的机载激光数据滤波方法

提出了基于距离限制的滤波算法，利用地物在三维空间的距离特性提取地面信息，并对可能影响算法稳健性的侧面信息提出角度判决的概念。试验表明，本文方法在处理复杂城市地形时，能很好地保留地面的细节信息，剔除低矮植被等非地面信息。     

复杂城市环境的机载Lidar点云滤波(英文)

A novel filtering algorithm for Lidar point clouds is presented, which can work well for complex cityscapes. Its main features are filtering based on raw Lidar point clouds without previous triangulation or rasterization. 3D topological relations among points are used to search edge points at the top of discontinuities, which are key information to recognize the bare earth points and building points. Experiment results show that the proposed algorithm can preserve discontinuous features in the bare earth and has no impact of size and shape of buildings.     

Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos

With the advent of unmanned aerial vehicles (UAVs) for mapping applications, it is possible to generate 3D dense point clouds using stereo images. This technology, however, has some disadvantages when compared to Light Detection and Ranging (LiDAR) system. Unlike LiDAR, digital cameras mounted on UAVs are incapable of viewing beneath the canopy, which leads to sparse points on the bare earth surface. In such cases, it is more challenging to remove points belonging to above-ground objects using ground filtering algorithms generated especially for LiDAR data. To tackle this problem, a methodology employing supervised image classification for filtering 3D point clouds is proposed in this study. A classified image is overlapped with the point cloud to determine the ground points to be used for digital elevation model (DEM) generation. Quantitative evaluation results showed that filtering the point cloud with this methodology has a good potential for high-resolution DEM generation.     

基于多分辨率方向预测的LIDAR点云滤波方法

为了快速提取LIDAR点云中的地面点,生成高精度的DTM,提出了一种基于多分辨率方向预测的LIDAR点云滤波方法。该方法首先构建多种分辨率数据集,然后基于方向预测法以分辨率由低到高的顺序逐层进行数据集的平滑处理,最后以最高分辨率数据集的平滑结果为基准标记原始LIDAR点云。本方法通过分析反距离权重插值模型的不足,利用改进的模型进行裸露地面点的插值,得到高精度的DTM。实验表明,本文方法能有效地滤除地物,并保持原有的地形特征,算法效率高,具有一定的实用价值。     

一种基于分割的机载LiDAR点云数据滤波

针对当前滤波算法在处理地形不连续区域或存在复杂建筑物区域时容易过分＂腐蚀＂地形并难以去除一些低矮植被的不足,提出了一种基于分割的机载LiDAR点云滤波算法。首先,对原始点云基于地表连续性进行分割;然后,在移除点数目较小的粗差点集之后采用对分割点集建立缓冲区的方法,区分地面和非地面点集;在较大地物经过迭代分割基本移除之后,使用约束平面的方法移除高度较小的地表附着物以实现滤波。实验结果表明,与经典滤波算法相比,该算法提高了地面点的分类精度,在滤除地物信息的同时能有效地保留地形特征。     

复杂城市环境的机载Lidar点云滤波

提出了一种新的Lidar点云滤波算法。该算法能对复杂的城市地貌进行滤波，无需事先进行三角网格化或栅格化，依靠点阃的拓扑关系直接对原始点云进行滤波。实验结果表明，该滤波方法能有效保留地形特征，且不受房屋形状和大小的影响。     

Digital Elevation Model from the Best Results of Different Filtering of a LiDAR Point Cloud

The LiDAR point clouds captured with airborne laser scanning provide considerably more information about the terrain surface than most data sources in the past. This rich information is not simply accessed and convertible to a high quality digital elevation model (DEM) surface. The aim of the study is to generate a homogeneous and high quality DEM with the relevant resolution, as a 2.5D surface. The study is focused on extraction of terrain (bare earth) points from a point cloud, using a number of different filtering techniques accessible by selected freeware. The proposed methodology consists of: (1) assessing advantages/disadvantages of different filters across the study area, (2) regionalization of the area according to the most suitable filtering results, (3) data fusion considering differently filtered point clouds and regions, and (4) interpolation with a standard algorithm. The resulting DEM is interpolated from a point cloud fused from partial point clouds which were filtered with multiscale curvature classification (MCC), hierarchical robust interpolation (HRI), and the LAStools filtering. An important advantage of the proposed methodology is that the selected landscape and datasets properties have been more holistically studied, with applied expert knowledge and automated techniques. The resulting highly applicable DEM fulfils geometrical (numerical), geomorphological (shape), and semantic quality properties.     

基于Otsu方法点云粗分类的渐进三角网滤波算法研究

针对传统渐进三角网滤波方法需要针对不同的地形条件频繁调整滤波参数,并且对低矮地物滤波效果较差等问题,结合图像分割中的Otsu方法,提出一种基于Otsu方法点云粗分类的渐进三角网滤波算法。在对原始点云数据粗分类的基础上,以点云类别属性引导滤波过程。实验结果表明,方法简单可行,可以有效地控制低矮点被误分类成地面点的可能性,提高滤波处理结果的准确性。     

基于Car(p,q)模型和数学形态学理论的LiDAR点云数据滤波

在分析现有的LiDAR点云数据后处理方法的基础上,本文提出了一种点云数据“分步”滤波方法。首先对LiDAR点云数据进行数学形态学“粗”滤波,得到“地面点假设”和“非地面点假设”。然后引入顾及因果关系的自回归模型（car）对两类点云数据假设进行模型化处理和假设检验,根据假设检验的结果判断地面点和非地面点,最终得到可靠的分类结果。与单纯的“最小二乘拟合预测法”或“数学形态学”方法相比,这种“分步”处理的思想用于LiDAR点云数据分类处理的结果更可靠。