Article(id=1242129350229819493, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242129295322186661, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2012.26.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1339084800000, receivedDateStr=2012-06-08, revisedDate=1343404800000, revisedDateStr=2012-07-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1347897600000, onlineDateStr=2012-09-18, pubDate=1347897600000, pubDateStr=2012-09-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1347897600000, onlineIssueDateStr=2012-09-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774076314740, creator=sys-migrate, updateTime=1774076314740, updator=sys-migrate, issue=Issue{id=1242129295322186661, tenantId=1146029695717560320, journalId=1146031591421210625, year='2012', volume='30', issue='26', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='1347897600000', pubDateStr='2012-09-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774076301648, creator='sys-migrate', updateTime=1774076301648, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=24, endPage=29, ext={EN=ArticleExt(id=1242129355690803332, articleId=1242129350229819493, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=A Window Iterative Kriging Algorithm for Filtering Airborne LiDAR Point Clouds, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=The first and significant step for processing airborne LiDAR is to remove non-terrain point clouds and reserve ground point clouds. According to the irregularities of elevation on LiDAR point clouds in the spatial distribution, a window iterative Kriging algorithm is proposed for filtering off objects from terrain point clouds. First of all, an elevation histogram of point clouds is used to filter low and high outliers. Then average point spacing is taken as the size of initial window, a Kriging interpolation method is adopted to fit the elevation of central grid by using the elevations of eight neighbor grids. If the height difference between fitting value and original height is larger than a height threshold, point clouds lying in the grid cell would be classified as object points. Then surplus points are interpolated into new grids with a size of window is twice as big as previous one. With the exponential increase of the size of window, surplus point clouds continue to be classified until the biggest window size is reached. Fifteen sampled data provided by ISPRS is used to test the method and eight other algorithms are compared with this method. The results show that type I error and total error of the method are less than the corresponding errors of most other methods. Therefore, the algorithm has some reference values for filtering LiDAR point clouds., authors=LI Feng, CUI Ximin, YUAN Debao, LIU Tiantian, TAN Xuehang, authorsList=LI Feng;CUI Ximin;YUAN Debao;LIU Tiantian;TAN Xuehang, authorCompany=College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=OLN3vgYIsDqxAa8eoMGDSA==, pdfFileSize=1795812, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242129354738700318, articleId=1242129350229819493, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=窗口迭代的克里金法过滤机载LiDAR点云, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=机载LiDAR点云处理的首要一步是过滤非地面点云而保留地面点云。根据LiDAR点云的高程值在空间分布的不规则性,一种窗口迭代的克里金法被用来过滤掉地物对象点。首先,通过点云的高程直方图滤除低位和高位的粗差点云。然后,以点云的平均点间距作为初始窗口大小,根据周围8邻域格网的高程值,使用克里金内插法拟合出中心格网的高程值;当拟合值与中心格网原始高程值之差大于设定的高差阈值时,中心格网内的点云就被归类为地物点,剩余未分类的点重新被内插成新的格网,窗口大小变为原来的2倍。随着窗口的不断增大,剩余的点被继续分类直到窗口达到最大为止。选取国际摄影测量与遥感协会(ISPRS)提供的15个样本数据测试这种算法,并与其他8种算法进行比对,结果发现窗口迭代的克里金法的I类误差和总误差较小,说明本算法在滤波方面具有一定的参考价值。, authors=李峰, 崔希民, 袁德宝, 刘甜甜, 谭雪航, authorsList=李峰;崔希民;袁德宝;刘甜甜;谭雪航, authorCompany=中国矿业大学(北京)地球科学与测绘工程学院,北京 100083, correspAuthors=null, authorNote=null, correspAuthorsNote=崔希民(中国科协所属全国学会个人会员登记号：E3824500005M),教授,研究方向为3S集成与应用、形变灾害监测及三维工业测量,电子信箱：cxm@cumtb.edu.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=yhthz4yzwvYMsf12OPZhmg==, pdfFileSize=1795812, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, 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