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国土资源遥感  2014, Vol. 26 Issue (4): 111-118    DOI: 10.6046/gtzyyg.2014.04.18
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
利用机载LiDAR数据提取与分析地裂缝
肖春蕾1,2, 郭兆成1, 张宗贵1, 李迁1, 尚博譞1, 吴芳1
1. 中国国土资源航空物探遥感中心, 北京 100083;
2. 国土资源部航空地球物理与遥感地质重点实验室, 北京 100083
Extraction and analysis of ground fissures from airborne LiDAR data
XIAO Chunlei1,2, GUO Zhaocheng1, ZHANG Zonggui1, LI Qian1, SHANG Boxuan1, WU Fang1
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Key Laboratory of Airborne Geophysics and Remote Sensing Geology, Ministry of Land and Resources, Beijing 100083, China
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摘要 机载激光扫描可获取植被茂密地区的数字地形模型(DTM),但将其用于茂密植被覆盖区地裂缝提取方法的研究还不多见。以湖南冷水江市浪石滩为试验区,基于机载LiDAR的激光点云数据,研究了植被覆盖区地裂缝的提取方法,分析了地裂缝的微地貌特征。首先对离散的三维激光点云数据依次进行基于不规则三角网滤波、高程滤波及回波信息强度滤波提取地面点,以保留完整的微地貌微特征; 然后构建不规则三角网,反距离加权内插生成数字高程模型(DEM),提取地裂缝识别参数,同时基于最小曲率对地裂缝进行线性探测,提取地裂缝的长度信息,且利用地裂缝剖面信息分析其微特征,结合识别参数分析地裂缝的稳定性。研究结果表明: 利用机载LiDAR点云数据提取的地裂缝识别参数,能够确定地裂缝的位置、坡度坡向、长度和深度信息,有助于判定地裂缝的稳定性; 在植被较为茂密、地面点密度稀疏的区域,保留一定的低矮植被所提取到的DEM能更好地保留地裂缝的微地貌特征。
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张转
马玉
蔡伟
关键词 高光谱图像波段分组排序Prim算法模糊贴近度最大最小贴近度(MMC)    
Abstract:Airborne laser scanning (ALS) data have been used to construct the digital terrain model under dense vegetation, but its reliability for recognition ground fissures in the tropics remains unknown. In this paper, Langshitan located in Lengshuijiang City was selected as the study area, and the method for extracting ground fissures and analyzing micro-topography features based on airborne LiDAR point cloud data in the dense vegetation were studied. First, the point clouds were separated into ground points and non-ground points through adaptive TIN filter method, elevation filter, echo intensity difference filter, to ensure that the bare-earth reserved micro-topography features in the dense vegetation. Second, on the basis of ground data, triangulated irregular network was built to generate digital elevation models by inverse distance weighted interpolation; afterwards, ground fissures identification parameters could be extracted, and then linear detection could be performed by the method of minimum curvature. Finally, ground fissures stability was analyzed by the profile information of LiDAR and identification parameters. The results achieved by the authors have shown that qualitative and quantitative identification parameters extracted by the LiDAR data can determine the location, slope and aspect as well as length and depth information of ground fissures and, on such a basis, ground fissures stability can be determined by the profile information of identification parameters and micro-topography features. It is thus proved that DEM constructed by ground points and low vegetation points could reserve micro-topography features.
Key wordshyperspectral image    band grouping and reordering    Prim algorithm    fuzzy similarity    maximum and minimum closeness(MMC)
收稿日期: 2013-08-14      出版日期: 2014-09-17
:  TP79  
基金资助:国土资源部航空地球物理与遥感地质重点实验室航遥青年创新基金项目(编号:2013YFL10)及中国地质调查局地质大调查项目“新型传感器矿山地质环境调查”(编号:1212011220083)共同资助。
作者简介: 肖春蕾(1987-),女,助理工程师,主要从事LiDAR、航空影像数据、数据处理及应用分析方面的研究。Email:45561247@qq.com。
引用本文:   
肖春蕾, 郭兆成, 张宗贵, 李迁, 尚博譞, 吴芳. 利用机载LiDAR数据提取与分析地裂缝[J]. 国土资源遥感, 2014, 26(4): 111-118.
XIAO Chunlei, GUO Zhaocheng, ZHANG Zonggui, LI Qian, SHANG Boxuan, WU Fang. Extraction and analysis of ground fissures from airborne LiDAR data. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 111-118.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.04.18      或      https://www.gtzyyg.com/CN/Y2014/V26/I4/111
[1] Jachens R C,Holzer T L.Differential compaction mechanism for earth fissures near Casa Grande,Arizona[J].Geological Society of America Bulletin,1982,93(10):998-1012.
[2] Holzer T L,Davis S N,Lofgren B E.Faulting caused by groundwater extraction in south-central Arizona[J].Journal of Geological Research,1979,84(B2):603-612.
[3] Belardinelli M E,Sandri L,Baldi P.The major event of the 1997 Umbria-Marche(Italy)sequence:What could we learn from DInSAR and GPS data[J].Geophysical Journal International,2003,153(1):242-252.
[4] Niebergall S,Loew A,Mauser W.Object-orientated analysis of very high resolution QuickBird data for mega city research in Delphi/India[C]//Proceedings of the Urban Remote Sensing Joint Event,Paris.ISBN:1-4244-0712-5.2007:8-13,IEEE07EX1577.
[5] Baltsavias E P.A comparison between photogrammetry and laser scanning[J].ISPRS J Photogrammetry and Remote Sensing,1999,54(2):83-94.
[6] Carter W E,Shrestha R L,Slatton K C.Geodetic laser scanning[J].Phys Today,2007:60(12):41-47.
[7] 张小红.机载激光雷达测量技术理论与方法[M].武汉:武汉大学出版社,2007:93-116。 Zhang X H.The Theory and Technical Method of Airborne Laser Radar Measurement[M].Wuhan:Wuhan University Process,2007:93-116.
[8] 路兴昌,张雪霞.基于回波强度和采样点距离的点云滤波研究[J].测绘科学,2009,34(6):196-197. Lu X C,Zhang X X.Study on points cloud filtering based on reflectance intensity and range[J].Science of Surveying and Mapping,2009,34(6):196-197.
[9] Niethammer U,Rothmund S,Joswig M.UAV-based remote sensing of the slow moving landslide Super-Sauze[C]//Malet J P,Rema tre A,Boogard T.Proceedings of the International Conference on Landslide Processes:From Geomorphologic Mapping to Dynamic Modelling.Strasbourg:CERG Press,2009:69-74.
[10] Niethammer U,Rothmund S,James M R,et al.UAV-based remote sensing of landslides[C]//International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences,Vol.XXXVIII,Part 5 Commission V Symposium,Newcastle upon Tyne,UK.2010:496-501.
[11] OrthoVista.Official OrthoVista software homepage[EB/OL].http://www.orthovista.com.(accessed 1 August 2010).
[12] Brügelmann R.Automatic breakline detection from airborne laser range data[J].International Archives of Photogrammetry and Remote Sensing,2000,33(B3):109-115.
[13] Briese C.Three-dimensional modelling of breaklines from airborne laser scanner data[J].International Archives of Photogrammetry Remote Sensing,2004,35(B3):109-I102.
[14] Wood J.The geomorphological characterization of digital elevation models[D].Leicester,UK:University of Leicester,1996.
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