A revised progressive TIN densification for filtering airborne LiDAR data

Abstract Filtering is an essential post-processing step for various applications of Light Detection and Ranging (LiDAR) data. Progressive triangular irregular network (TIN) densification (PTD) is a commonly used algorithm for filtering airborne discrete-return LiDAR data. However, this method has limitations in removing point clouds belonging to lower objects and preserving ground measurements in topographically complex areas. Therefore, this study revised the classic PTD method by building an improved TIN and changing the original iterative judgment criterions for better filtering airborne LiDAR point clouds. Similar to the classic PTD method, our revised PTD method also consists of three core steps: parameter specification, seed point selection and initial TIN construction, and iterative densification of TIN. To evaluate the performance of our revised PTD method, it was applied to benchmark datasets provided by ISPRS Working Group III/3, and compared with the classic PTD method in filtering airborne LiDAR data. Experimental results indicated that, our revised PTD approach performed better than the classic PTD method in preserving ground points in steep areas and removing non-ground points which belong to lower objects. Additionally, results showed that our revised PTD method is capable of reducing Type I errors, Type II errors and total errors by 10.26%, 0.79% and 8.07% respectively. Our revised PTD method offers a better solution for filtering airborne LiDAR discrete-return data.

[1]  Murat Uysal,et al.  Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation , 2015 .

[2]  C. Brenner,et al.  A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data , 2013 .

[3]  Thomas Blaschke,et al.  An object-based analysis filtering algorithm for airborne laser scanning , 2012 .

[4]  Norbert Pfeifer,et al.  Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain☆ , 2007 .

[5]  Ran Wang,et al.  A progressive morphological filter for point cloud extracted from UAV images , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[6]  Jing Shen,et al.  Extraction of individual tree crowns from airborne LiDAR data in human settlements , 2013, Math. Comput. Model..

[7]  Xiaoye Liu,et al.  Airborne LiDAR for DEM generation: some critical issues , 2008 .

[8]  Xiangguo Lin,et al.  Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments , 2014, Remote. Sens..

[9]  George Vosselman,et al.  Two algorithms for extracting building models from raw laser altimetry data , 1999 .

[10]  Domen Mongus,et al.  Parameter-free ground filtering of LiDAR data for automatic DTM generation , 2012 .

[11]  A. S. Toprak,et al.  An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area , 2015 .

[12]  Xiangyun Hu,et al.  Remote sensing , 2020, Water Resources in the Mediterranean Region.

[13]  Junichi Susaki,et al.  Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation , 2012, Remote. Sens..

[14]  Xiangguo Lin,et al.  Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification , 2013 .

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

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

[17]  N. Glenn,et al.  LiDAR measurement of sagebrush steppe vegetation heights , 2006 .

[18]  Z. Vojinovic,et al.  LIDAR FILTERING ALGORITHMS FOR URBAN FLOOD APPLICATION: REVIEW ON CURRENT ALGORITHMS AND FILTERS TEST , 2009 .

[19]  Ruisheng Wang,et al.  3D building modeling using images and LiDAR: a review , 2013 .

[20]  Jessica J. Mitchell,et al.  Errors in LiDAR-derived shrub height and crown area on sloped terrain , 2011 .

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

[22]  Qing Zhu,et al.  An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy , 2014 .

[23]  Cheng Wang,et al.  Separation of Ground and Low Vegetation Signatures in LiDAR Measurements of Salt-Marsh Environments , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

[25]  Yi-Hsing Tseng,et al.  Automatic Segmentation of Lidar Data into Coplanar Point Clusters Using an Octree-Based Split-and-Merge Algorithm , 2010 .

[26]  Antonio Luis Montealegre,et al.  A Comparison of Open-Source LiDAR Filtering Algorithms in a Mediterranean Forest Environment , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Fan-Rui Meng,et al.  Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification , 2008 .

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

[29]  Xianyu Yu,et al.  An Improved Morphological Algorithm for Filtering Airborne LiDAR Point Cloud Based on Multi-Level Kriging Interpolation , 2016, Remote. Sens..

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

[31]  Temuulen Tsagaan Sankey,et al.  LiDAR-Based Classification of Sagebrush Community Types , 2011 .

[32]  B. Koch,et al.  Detection of individual tree crowns in airborne lidar data , 2006 .

[33]  Norbert Pfeifer,et al.  A Comparison of Evaluation Techniques for Building Extraction From Airborne Laser Scanning , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  George Vosselman,et al.  FILTERING OF AIRBORNE LASER SCANNER DATA BASED ON SEGMENTED POINT CLOUDS , 2005 .

[35]  C. Gregoretti,et al.  Comparative analysis of the differences between using LiDAR and contour-based DEMs for hydrological modeling of runoff generating debris flows in the Dolomites , 2015, Front. Earth Sci..

[36]  Ziyue Chen,et al.  Upward-fusion urban DTM generating method using airborne Lidar data , 2012 .

[37]  Keith C. Clarke,et al.  An improved simple morphological filter for the terrain classification of airborne LIDAR data , 2013 .

[38]  Xiangguo Lin,et al.  Object-Based Classification of Airborne Light Detection and Ranging Point Clouds in Human Settlements , 2012 .

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

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

[41]  K. Mengersen,et al.  Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species , 2005 .