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利用既有知识渐近数学形态学提取LiDAR数据中道路信息方法研究 被引量:3

Method of extraction road information in LIDAR data based on the existing knowledge of asymptotic mathematical morphology
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摘要 为了避免由于内插和构网或是局限于局部分析所带来的地形失真,本文根据道路先验知识的获取只需对实验道路进行统计学分析,无论从技术上,还是仪器设备上都简单、方便、快捷;在各种LIDAR数据滤波与分类方法的基础上,提出了一种在原始离散点集中提取道路信息的基于既有知识数学形态学分类方法。实验结果显示,为探索LIDAR数据在交通工程中的应用,加快道路的信息化建设,提供了一种可供借鉴的方法。 To avoid the terrain distortion caused by the interpolation and limited to local analysis or network configuration, and to ensure simple, convenient and fast operation no matter with technology or equipment and instruments by making a statistical analysis for the tested road to access a priori knowledge of the road, a new method about concentratively extracting road information among the origi- nal discrete points based on the existing knowledge of mathematical morphology classification was introduced, which was brought up from a variety methods of filtering and classification of LIDAR data. The results showed that it provided a way for reference in the exploration of using LIDAR data in traffic engineering and speeding up road information technology construction.
出处 《测绘科学》 CSCD 北大核心 2010年第4期154-156,共3页 Science of Surveying and Mapping
关键词 既有知识渐近数学形态学 LIDAR数据 道路信息 existing knowledge of asymptotic mathematical morphology LIDAR road information
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  • 1赖旭东,万幼川.一种针对激光雷达强度图像的滤波算法研究[J].武汉大学学报(信息科学版),2005,30(2):158-160. 被引量:17
  • 2林怡,陈鹰.用立体影像匹配和数学形态变换自动生成DEM[J].中国图象图形学报(A辑),2003,8(4):447-452. 被引量:11
  • 3刘经南,张小红.利用激光强度信息分类激光扫描测高数据[J].武汉大学学报(信息科学版),2005,30(3):189-193. 被引量:65
  • 4梁欣廉,张继贤,李海涛,闫平.激光雷达数据特点[J].遥感信息,2005,27(3):71-76. 被引量:58
  • 5[2]Haaala N,Brenner K.Generation of 3D city models from airborne laser scanning data.Proceedings EARSEL Workshop on LIDAR remote sensing on land and area,Tallinn/Estonia,1997. 被引量:1
  • 6[3]Haala N,Brenner C.Extraction of building and trees in urban environments[A].In:ISPRS Journal of photogrammetry and remote sensing[C],1999,54 (2/3):130-137. 被引量:1
  • 7[4]Lemmens M,Deijkers H,Looman P.Building detection by fusion airborne laser-altimeter DEMs and 2D digital maps[J].International Archives of Photogrammetry and Remote Sensing,1997,32,(3-4):42-49. 被引量:1
  • 8[5]Hug Ch,Wehr A.Detecting and identifying topographic objects in imaging laser altimetry data[J].International Archives of Photogrammetry and Remote Sensing,1997,32,(3-4):19-26. 被引量:1
  • 9[6]Maas H-G,Vosselman G.Two algorithms for extracting building models from raw laser altimetry data[A].In:ISPRS Journal of photogrammetry and remote sensing[C],1999,54(2/3):245-261. 被引量:1
  • 10[7]Haala N,Brenner C,Statter C.An integrated system for urban model generation[J].International Archives of Photogrammetry and remote sensing,1998,32,Part Ⅱ. 被引量:1

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  • 1刘经南,张小红.利用激光强度信息分类激光扫描测高数据[J].武汉大学学报(信息科学版),2005,30(3):189-193. 被引量:65
  • 2程起敏,杨崇俊,邵振峰.基于多进制小波变换的渐进式纹理图像检索[J].武汉大学学报(信息科学版),2005,30(6):521-524. 被引量:10
  • 3倪伟,郭宝龙,杨镠.图像多尺度几何分析新进展:Contourlet[J].计算机科学,2006,33(2):234-236. 被引量:20
  • 4林祥国.面向对象的机载LiDAR点云分析理论和方法[R].北京:中国测绘科学研究院,2012. 被引量:1
  • 5APARAJITHAN S, JIE S, Segmentation and Recon- struction of Polyhedral Building Roofs From Aerial Li- dar Point Clouds EC]//IEEE Transactions on Geosci- ence and Remote Sensing. IEEE, 2010, 48 (3): 1554-1567. 被引量:1
  • 6VOSSELMAN G,GORTE B G H,SITHOLE G,et al. Recongnizing Structure in Laser Scanner Point Clouds [J]. International Archives of the Photogrammetry, Re- mote Sensing and Spatial Information Sciences, 2004,36 (8/W2) :33-38. 被引量:1
  • 7FUALVEL M,TARABALKA Y,BENNEDIKTSSON J A,et al.Advances in Spectral-spatial Classification of Hyper-spectral Images[C]//Proceedings of IEEE,2013,101(3):652-675. 被引量:1
  • 8JIN X,DAVIS C H.Automated Building Extraction from High-resolution Satelite Imagery in Urban Areas Using Structural,Contextual,and Spectral Information[J].EURASIP Journal on Applied Signal Processing,2005(14):2196-2206. 被引量:1
  • 9LEE D S,SHAN J,BETHEL J S.Class-guided Building Extraction from Ikonos Imagery[J].Photogrammetric Engineering&Remote Sensing,2003,69(2):153-150. 被引量:1
  • 10HUANG X,ZHANG L.An SVM Ensemble Approach Combing Spectral,Structural,and Semantic Features for the Classification of High-resolution Remotely Sensed Imagery[C]//IEEE Transaction on Geoscience and Remote Sensing,2013,51(1):257-272. 被引量:1

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