机载LIDAR数据滤波及建筑物提取技术研究
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摘要
机载激光雷达(Light Detection and Ranging-LIDAR)是一种激光对地扫描获取地形表面空间和特征信息的直接定位技术。作为一种新型的对地测绘手段,机载LIDAR不仅解决了传统航空摄影测量测绘地形困难区域的难题,还在一定程度上指引着今后对地观测技术的发展方向。本文重点探讨点云数据滤波和建筑物提取技术,提出了一种坡度自适应滤波算法和建筑物自动提取的处理流程,并通过实验进行具体验证,得出了一些有益的结论。
本文的主要工作和研究重点如下:
1.介绍了机载LIDAR的系统组成、数据特点和系统误差源,并与航空摄影测量技术进行了比较。回顾了一些经典的数据滤波算法,对这些算法进行了分析和归纳,详细介绍了基于表面、基于区域和基于坡度等三种类型的滤波算法及其特性。简要回顾了基于点云数据的建筑物提取进展,并对其中需要解决的问题进行了总结。
2.提出了一种坡度自适应滤波算法。该算法是基于CAS模型的坡度滤波算法,利用了4种坡度阈值:坡度、坡度增量、最小坡度和最大坡度,作为选取地面点的判断依据,克服了坡度滤波单纯依赖坡度及坡度变化选取地面点的缺陷。对初始地形的估计,为算法实施提供了比较准确的滤波参数。点云数据的滤波实验结果表明,该算法具有较强的自适应性和稳定性。
3.提出了一种使用机载LIDAR激光点云的首次回波数据和多光谱影像的建筑物自动提取策略。从点云数据中提取建筑物,是在分析建筑物点云特征的基础上,综合运用了数学形态学、模式识别等技术办法。在候选建筑物区域,提取出基于区域的灰度级共生矩阵(Gray Level Cooccurrence Matrix-GLCM)纹理特征,运用聚类算法进行纹理特征的聚类,解决了区分建筑物与高大树木的难题。此外,还研究了由多光谱影像生成的规则化差分植被指数(Normalized Difference Vegetation Index-NDVI)影像作为辅助数据,用于建筑物提取的实际作用。
Airborne LIDAR (Light Detection and Ranging) orientates terrain’s positions directly and acquires terrain surface’s geo-coordinates and characteristics by scanning the terrain with laser. As a newly emerged instrument of terrain mapping, not only airborne LIDAR solves the problem of mapping difficult regions by traditional aerial photogrammetry, but also puts some influence on the development of terrain mapping. The aim of this dissertation focuses on two tough problems: point clouds filtering when obtaining digital elevation models (DEMs) and building extraction when modeling urban buildings with airborne LIDAR data. A filtering algorithm called slope slef-adaptive filtering and an automatic building extraction strategy based on airborne LIDAR data are presented in this dissertation.
The primary works and innovations are included as:
1. The components of airborne LIDAR system, LIDAR data’s characteristics and LIDAR system errors are introduced firstly, and then the airborne photogrammetry technique is compared with airborne LIDAR. Some classical point clouds filtering algorithms are reviewed, and by analysis and conclusion, these algorithms are sorted into three types: surface based, region based and slope based. The methods and algorithms used in building extraction are also reviewed, and a few problems that need to be settled are summarized.
2. A slope self-adaptive filtering algorithm is put forward. The idea of this algorithm is from CAS filtering model, but makes some improvement. There are four types of slope thresholds in this algorithm, which are general slope, slope increment, minimum slope and maximum slope. All these slopes are used as constraint condition when searching for ground points, thus, overcome the disadvantage of searching for ground points only with slope and slope change thresholds. The terrain’s slope is estimated priorly, so the filtering algorithm can acquire more accurate slope thresholds. The experiment result validates the algorithm being self-adaptive and stable.
3. An automatic building extraction strategy is presented that uses first echo of airborne LIDAR point clouds and multi-spectral image. Characteristics of buildings in the LIDAR point clouds are learned first. Some knowledge or theory, such as mathematical morphology, digital image processing and pattern recognition are also utilized in processing. For each building candidate, the gray level coorcurrence matrix (GLCM) texture features are extracted, buildings and trees are identified by clustering algorithms using these features. The normalized difference vegetation index (NDVI) image is generated from multi-spectral image, and then it is used as assistant data for building extraction.
本文的主要工作和研究重点如下:
1.介绍了机载LIDAR的系统组成、数据特点和系统误差源,并与航空摄影测量技术进行了比较。回顾了一些经典的数据滤波算法,对这些算法进行了分析和归纳,详细介绍了基于表面、基于区域和基于坡度等三种类型的滤波算法及其特性。简要回顾了基于点云数据的建筑物提取进展,并对其中需要解决的问题进行了总结。
2.提出了一种坡度自适应滤波算法。该算法是基于CAS模型的坡度滤波算法,利用了4种坡度阈值:坡度、坡度增量、最小坡度和最大坡度,作为选取地面点的判断依据,克服了坡度滤波单纯依赖坡度及坡度变化选取地面点的缺陷。对初始地形的估计,为算法实施提供了比较准确的滤波参数。点云数据的滤波实验结果表明,该算法具有较强的自适应性和稳定性。
3.提出了一种使用机载LIDAR激光点云的首次回波数据和多光谱影像的建筑物自动提取策略。从点云数据中提取建筑物,是在分析建筑物点云特征的基础上,综合运用了数学形态学、模式识别等技术办法。在候选建筑物区域,提取出基于区域的灰度级共生矩阵(Gray Level Cooccurrence Matrix-GLCM)纹理特征,运用聚类算法进行纹理特征的聚类,解决了区分建筑物与高大树木的难题。此外,还研究了由多光谱影像生成的规则化差分植被指数(Normalized Difference Vegetation Index-NDVI)影像作为辅助数据,用于建筑物提取的实际作用。
Airborne LIDAR (Light Detection and Ranging) orientates terrain’s positions directly and acquires terrain surface’s geo-coordinates and characteristics by scanning the terrain with laser. As a newly emerged instrument of terrain mapping, not only airborne LIDAR solves the problem of mapping difficult regions by traditional aerial photogrammetry, but also puts some influence on the development of terrain mapping. The aim of this dissertation focuses on two tough problems: point clouds filtering when obtaining digital elevation models (DEMs) and building extraction when modeling urban buildings with airborne LIDAR data. A filtering algorithm called slope slef-adaptive filtering and an automatic building extraction strategy based on airborne LIDAR data are presented in this dissertation.
The primary works and innovations are included as:
1. The components of airborne LIDAR system, LIDAR data’s characteristics and LIDAR system errors are introduced firstly, and then the airborne photogrammetry technique is compared with airborne LIDAR. Some classical point clouds filtering algorithms are reviewed, and by analysis and conclusion, these algorithms are sorted into three types: surface based, region based and slope based. The methods and algorithms used in building extraction are also reviewed, and a few problems that need to be settled are summarized.
2. A slope self-adaptive filtering algorithm is put forward. The idea of this algorithm is from CAS filtering model, but makes some improvement. There are four types of slope thresholds in this algorithm, which are general slope, slope increment, minimum slope and maximum slope. All these slopes are used as constraint condition when searching for ground points, thus, overcome the disadvantage of searching for ground points only with slope and slope change thresholds. The terrain’s slope is estimated priorly, so the filtering algorithm can acquire more accurate slope thresholds. The experiment result validates the algorithm being self-adaptive and stable.
3. An automatic building extraction strategy is presented that uses first echo of airborne LIDAR point clouds and multi-spectral image. Characteristics of buildings in the LIDAR point clouds are learned first. Some knowledge or theory, such as mathematical morphology, digital image processing and pattern recognition are also utilized in processing. For each building candidate, the gray level coorcurrence matrix (GLCM) texture features are extracted, buildings and trees are identified by clustering algorithms using these features. The normalized difference vegetation index (NDVI) image is generated from multi-spectral image, and then it is used as assistant data for building extraction.
引文
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[30]Hu Y., Tao C. V. Hierarchical Recovery of Digital Terrain Models from Single and Multiple Return LiDAR Data[J]. Photogrammetry Engineering & Remote Sensing. 2005, 71: 425-433.
[31]Axelsson P. Processing of Laser Scanner Data– Algorithms and Applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54: 138-147.
[32]Kilian J., N. Haala, M. Englich. Capture and Evaluation of Airborne Laser Scanner Data[J]. International Archives of Photogrammetry and Remote Sensing, 1996, 31: 383-388.
[33]Wack R. Wimmer A. Digital Terrain Models from Airborne Laser Scanner Data-A Grid Based Approach[C]. ISPRS Commission III, Symposium, Graz, Austria, 2002.
[34]Zhang K. Q., Chen S. C. A Progressive Morphological Filter for Removing Non-ground Measurements from Airborne LIDAR Data[J]. IEEE Tractions on Geoscience and Remote Sensing, 2003, 41: 871-882.
[35]Sithole G. Filtering of Laser Altimetry Data Using a Slope Adaptive Filter[C]. IAPRS, 2001, Annapolis, MD, 34(3/W4):203-210.
[36]Meng X. A Slope and Elevation Based Filter to Remove Non-ground Measurements from Airborne LIDAR Data[C]. ISPRS WG III/3, III/4, V/3 Workshop“Laser scanning 2005”, the Netherlands, 2005.
[37]Elmqvist M. Ground Estimation of Laser Radar Data Using Active Shape Models[R]. Proc. OEEPE Workshop on Airborne Laserscanning and Interferometric SAR for Detailed Digital Elevation Models, OEEPE Publication, 2001.
[38]Elmqvist M., Jungert E., Lantz F., Persson A. Soderman U. Terrain Modeling and analysis Using Laser Scanner Data[C]. IAPRS, Annapolis, 2001.
[39]Sohn G., Dowman I. Terrain Surface Reconstruction by the Use of Tetrahedron Model with the MDL Criterion[C]. IAPRS, ISPRS Commission III, Symposium, Graz, Austria, 2002.
[40]Roggero M. Airborne Laser Scanning: Clustering in Raw Data[C]. IAPRS, Annapolis, Maryland, 2001.
[41]Brovelli M. A., Cannata M., Longoni U. M. Managing and Processing LIDAR Data within GRASS[C]. Proceedings of the GRASS Users Conference, Trento, Italy, 2002.
[42]Axelsson P. DEM Generation from Laser Scanner Data Using Adaptive TIN Models[J]. In IAPRS, 2000, 33: 110-117.
[43]Axelsson P. Ground Estimation of Laser Data Using Adaptive TIN-models[R]. Proceedings of OEEPE workshop on airborne lasercanning and interferometric SAR for detailed digital elevation models, 2001: 185-208.
[44]Rottensteiner F., C. Briese. A New Method for Building Extraction in Urban Areas from High-resolution Lidar Data[C]. International Archives of Photogrammetry and Remote Sensing, 2002.
[45]Hofmann A. D., H. Mass, A. Strsilein. Knowledge-based Building Detection Based on Laser Scanner Data and Topographic Map Information[J]. International Archives of Photogrammetry and Remote Sensing, 2002, 34: 163-169.
[46]Matikainen L., J. Hyypp?, H. Hyypp?. Automatic Detection of Buildings from Laser Scanner Data for Map Updating[C]. Workshop 3-D reconstruction from airborne lasrscanner and InSAR data, Dresden, Germany, 2003: 218-224.
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[2] Fourcade: the Centenary of a Stereoscopic Method of Photographic Surveying[EB/OL]. URL: http://www3.interscience.wiley.com/journal/119019451/abstract?CRETRY=1&SRETRY=0, 2008-12-30
[3] M. Patrick McCormick, Chris A. Hostetler. LITE-The First Spaceborne Lidar[C]. Combined Optical-Microwave Earth Atmosphere Sensing, Conference Proceedings, Second Topical Symposium, 1995: 163-166.
[4] Ackermann, F. Airborne Laser Scanning-Present Status and Future Expectation[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 1999, 54: 64-67.
[5] Baltsavias, E. P. A Comparison Between Photogrammetry and Laser Scanning[J], ISPRS Journal of Photogrammetry & Remote Sensing, 1999, 54: 83–94.
[6]刘经南,张小红.激光扫描测高技术的发展与现状[J].武汉大学学报·信息科学版,2003,28: 132-137.
[7] Tao, V., Hu, Y. A Review of Post-processing Algorithms for Airborne LIDAR Data[C], ASPRS Annual Conference, St. Louis, 2001.
[8]纪松.线阵影像多视匹配自动提取DSM的理论与方法[D].信息工程大学硕士学位论文,2008.
[9] E. P. Baltsavias. Airborne Laser Scanning: Existing Systems and Firms and Other Resources[J], ISPRS JPRS, 1999, 54: 164-198.
[10]E. P. Baltsavias. Airborne Laser Scanning-Relations and Formulas[J], ISPRS JPRS, 1999, 54: 199-214.
[11]Vosselman, G. Slope Based Filtering of Laser Altimeter Data[C]. IAPRS, Amsterdam, 2000: 935-942.
[12]Pfeifer N., Stadler P., Briese C. Deriving of Digital Terrain Models in the SCPP++ Environment[C]. OEEPE Workshop on Airborne Laserscanning and InSAR for Detailed DEMs, Stockholm, 2001.
[13]张小红.机载激光扫描测高数据滤波及地物提取[D].武汉大学博士学位论文,2002.
[14]Vosselman G. Building Reconstruction Using Planar Faces in Very High-density Data[C]. IAPRS, Munich, 1999.
[15]Vosselman G., Dijkman S. 3-D Building Moel Reconstruction from Point Clouds and Ground Plans[J]. IAPRS, 2001, 34: 37-44.
[16]Bharat Lohani. Airborne Altimetric LiDAR: Principle, Data Collection, Processing and Applications[EB]. URL: http://home.iitk.ac.in/~blohani/LiDAR_Tutorial/Airborne _AltimetricLidar.htm , 2008-10-5.
[17]Joe Liadsky. Introduction to LIDAR[R/OL].URL: http://www.optech.ca, 2008-5-23.
[18]LAS Specification[R/OL].URL: http://www.ASPRS.org, 2008-3-3.
[19]Yong Hu. Automated Extraction of Digital Terrain Models, Roads and Buildings Using Airborne Lidar Data[D]. Ph D. Dissertation, University of Calgary, 2003.
[20]Burtch, R. Lidar Principles and Applications[C]. IMAGIN Conference: Geography on the Move, Travers, MI, 2002.
[21]Sithole G., G. Vosselman. Comparison of Filtering Algorithms[R]. Workshop: 3-D reconstruction from airborne laserscanner and InSAR data, 2003.
[22]Mohamed A. Navigation the Ground from Air: Active Monitoring with GPS/INS Geo-reference Lidar[C]. ION Conference, Anaheim, 2003.
[23]Katzenbeisser R. About the Calibration of Lidar Sensors[C]. ISPRS Workshop on 3-D Reconstruction from Airborne Laserscanner and InSAR Data, Dresden, Germany, 2003.
[24]Kornus W., A. Ruiz. Strip Adjustment of LIDAR Data[C]. ISPRS Workshop on 3-D Reconstruction from Airborne Laserscanner and InSAR Data, Dresden, Germany, 2003
[25]ALTM Airborne Laser Terrain Mappers, URL: http://www.optech.ca, 2009-1-13.
[26]Flood M. LIDAR Activities and Research Priorities in the Commercial Sector[C]. International Archives of Photogrammetry and Remote Sensing, Annapolis, Maryland, 2001.
[27]Kraus K. Pfeifer N. Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data[J]. ISPRS JPRS, 1998, 53: 193-203.
[28]Pfeifer N. Kostli A. Kraus K. Interpolation and Filtering of Laser Scanner Data– Implementation and First Results[C]. International archives of photogrammetry and remote sensing, 1998, Columbus, 32: 153-159.
[29]Pfeifer N. Stadler P. Derivation of Digital Terrain Models in the SCOP Environment[R]. Proceedings of OEEPE workshop on airborne lasercanning and interferometric SAR for detailed digital dlevation models, 2001.
[30]Hu Y., Tao C. V. Hierarchical Recovery of Digital Terrain Models from Single and Multiple Return LiDAR Data[J]. Photogrammetry Engineering & Remote Sensing. 2005, 71: 425-433.
[31]Axelsson P. Processing of Laser Scanner Data– Algorithms and Applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54: 138-147.
[32]Kilian J., N. Haala, M. Englich. Capture and Evaluation of Airborne Laser Scanner Data[J]. International Archives of Photogrammetry and Remote Sensing, 1996, 31: 383-388.
[33]Wack R. Wimmer A. Digital Terrain Models from Airborne Laser Scanner Data-A Grid Based Approach[C]. ISPRS Commission III, Symposium, Graz, Austria, 2002.
[34]Zhang K. Q., Chen S. C. A Progressive Morphological Filter for Removing Non-ground Measurements from Airborne LIDAR Data[J]. IEEE Tractions on Geoscience and Remote Sensing, 2003, 41: 871-882.
[35]Sithole G. Filtering of Laser Altimetry Data Using a Slope Adaptive Filter[C]. IAPRS, 2001, Annapolis, MD, 34(3/W4):203-210.
[36]Meng X. A Slope and Elevation Based Filter to Remove Non-ground Measurements from Airborne LIDAR Data[C]. ISPRS WG III/3, III/4, V/3 Workshop“Laser scanning 2005”, the Netherlands, 2005.
[37]Elmqvist M. Ground Estimation of Laser Radar Data Using Active Shape Models[R]. Proc. OEEPE Workshop on Airborne Laserscanning and Interferometric SAR for Detailed Digital Elevation Models, OEEPE Publication, 2001.
[38]Elmqvist M., Jungert E., Lantz F., Persson A. Soderman U. Terrain Modeling and analysis Using Laser Scanner Data[C]. IAPRS, Annapolis, 2001.
[39]Sohn G., Dowman I. Terrain Surface Reconstruction by the Use of Tetrahedron Model with the MDL Criterion[C]. IAPRS, ISPRS Commission III, Symposium, Graz, Austria, 2002.
[40]Roggero M. Airborne Laser Scanning: Clustering in Raw Data[C]. IAPRS, Annapolis, Maryland, 2001.
[41]Brovelli M. A., Cannata M., Longoni U. M. Managing and Processing LIDAR Data within GRASS[C]. Proceedings of the GRASS Users Conference, Trento, Italy, 2002.
[42]Axelsson P. DEM Generation from Laser Scanner Data Using Adaptive TIN Models[J]. In IAPRS, 2000, 33: 110-117.
[43]Axelsson P. Ground Estimation of Laser Data Using Adaptive TIN-models[R]. Proceedings of OEEPE workshop on airborne lasercanning and interferometric SAR for detailed digital elevation models, 2001: 185-208.
[44]Rottensteiner F., C. Briese. A New Method for Building Extraction in Urban Areas from High-resolution Lidar Data[C]. International Archives of Photogrammetry and Remote Sensing, 2002.
[45]Hofmann A. D., H. Mass, A. Strsilein. Knowledge-based Building Detection Based on Laser Scanner Data and Topographic Map Information[J]. International Archives of Photogrammetry and Remote Sensing, 2002, 34: 163-169.
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