Threshold-free object and ground point separation in LIDAR data

Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as ''hilly'' or ''moderate'' terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.

[1]  Hans-Gerd Maas.  Akquisition von 3D-GIS-Daten durch Flugzeug-Laserscanning , 2005, KN - Journal of Cartography and Geographic Information.

[2]  P. Gong,et al.  Filtering airborne laser scanning data with morphological methods , 2007 .

[3]  Lukas Zebedin,et al.  Towards 3D map generation from digital aerial images , 2006 .

[4]  P. Mather,et al.  Classification Methods for Remotely Sensed Data , 2001 .

[5]  Ulf Söderman,et al.  ON MODELLING AND VISUALISATION OF HIGH RESOLUTION VIRTUAL ENVIRONMENTS USING LIDAR DATA , 2004 .

[6]  J. Osborne,et al.  Sample size and subject to item ratio in principal components analysis. , 2004 .

[7]  G. Sithole.  FILTERING OF LASER ALTIMETRY DATA USING A SLOPE ADAPTIVE FILTER , 2001 .

[8]  David G. Stork,et al.  Pattern Classification , 1973 .

[9]  M. Bartels,et al.  Segmentation of LIDAR data using measures of distribution , 2006 .

[10]  James E. Bartlett,et al.  Organizational research: Determining appropriate sample size in survey research , 2001 .

[11]  Chengcui Zhang,et al.  A progressive morphological filter for removing nonground measurements from airborne LIDAR data , 2003, IEEE Trans. Geosci. Remote. Sens..

[12]  K. Kraus,et al.  Determination of terrain models in wooded areas with airborne laser scanner data , 1998 .

[13]  E. Baltsavias,et al.  Automatic Extraction of Man-Made Objects from Aerial and Space Images (II) , 1995 .

[14]  Juha Hyyppä,et al.  USING INDIVIDUAL TREE CROWN APPROACH FOR FOREST VOLUME EXTRACTION WITH AERIAL IMAGES AND LASER POINT CLOUDS , 2005 .

[15]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[16]  U. Stilla,et al.  Automatic vehicle extraction from airborne LiDAR data of urban areas using morphological reconstruction , 2008, 2008 IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008).

[17]  D. Mason,et al.  Image processing of airborne scanning laser altimetry data for improved river flood modelling , 2001 .

[18]  I. Dowman,et al.  Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction * , 2007 .

[19]  Hong Wei,et al.  Unsupervised Segmentation Using Gabor Wavelets and Statistical Features in LIDAR Data Analysis , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[20]  Hong Wei,et al.  DTM Generation from LIDAR Data using Skewness Balancing , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[21]  Peter Axelsson,et al.  Processing of laser scanner data-algorithms and applications , 1999 .

[22]  George Vosselman,et al.  Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds , 2004 .

[23]  N. Pfeifer,et al.  DERIVATION OF DIGITAL TERRAIN MODELS IN THE SCOP++ ENVIRONMENT , 2001 .

[24]  M. Elmqvist,et al.  TERRAIN MODELLING AND ANALYSIS USING LASER SCANNER DATA , 2001 .

[25]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[26]  E. J. Huising,et al.  Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications , 1998 .

[27]  Uwe Weidner.  Digital Surface Models for Building Extraction , 1997 .

[28]  H. Maas,et al.  THE USE OF ANISOTROPIC HEIGHT TEXTURE MEASURES FOR THE SEGMENTATION OF AIRBORNE LASER SCANNER DATA , 2000 .

[29]  John Trinder,et al.  Building detection by fusion of airborne laser scanner data and multi-spectral images : Performance evaluation and sensitivity analysis , 2007 .

[30]  Claus Brenner.  TOWARDS FULLY AUTOMATIC GENERATION OF CITY MODELS , 2000 .

[31]  George Vosselman,et al.  3D BUILDING MODEL RECONSTRUCTION FROM POINT CLOUDS AND GROUND PLANS , 2001 .

[32]  A. R. H. Swan,et al.  Introduction to Geological Data Analysis , 1995 .

[33]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .