Performance analysis of freeware filtering algorithms for determining ground surface from airborne laser scanning data

Abstract Numerous filtering algorithms have been developed in order to distinguish the ground surface from nonground points acquired by airborne laser scanning. These algorithms automatically attempt to determine the ground points using various features such as predefined parameters and statistical analysis. Their efficiency also depends on landscape characteristics. The aim of this contribution is to test the performance of six common filtering algorithms embedded in three freeware programs. The algorithms’ adaptive TIN, elevation threshold with expand window, maximum local slope, progressive morphology, multiscale curvature, and linear prediction were tested on four relatively large (4 to 8     km 2 ) and diverse landscape areas, which included steep sloped hills, urban areas, ridge-like eskers, and a river valley. The results show that in diverse test areas each algorithm yields various commission and omission errors. It appears that adaptive TIN is suitable in urban areas while the multiscale curvature algorithm is best suited in wooded areas. The multiscale curvature algorithm yielded the overall best results with average root-mean-square error values of 0.35 m.

[1]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

[3]  Aive Liibusk,et al.  Determining sea surface heights using small footprint airborne laser scanning , 2013, Remote Sensing.

[4]  Kaiguang Zhao,et al.  Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues , 2010, Remote. Sens..

[5]  Halim Setan,et al.  DTM generation from LiDAR data by using different filters in open-source software , 2010 .

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

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

[8]  M. Brovelli,et al.  Filtering LiDAR with GRASS: overview of the method and comparisons with Terrascan , 2011 .

[9]  Kaizhong Zhang,et al.  Airborne LIDAR Data Processing and Analysis Tools , 2007 .

[10]  G. Vosselman.  SLOPE BASED FILTERING OF LASER ALTIMETRY DATA , 2000 .

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

[12]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  D. Whitman,et al.  Comparison of Three Algorithms for Filtering Airborne Lidar Data , 2005 .

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