DecHPoints: A New Tool for Improving LiDAR Data Filtering in Urban Areas

Identifying ground points from LiDAR data remains a challenge more than 2 decades after automatic filtering methods were first developed. The efficacy of filtering methods depends on both the physical characteristics of the environment and on the quality of the data used. Other limitations, affecting accessibility and usability, include the choice of filter and identification of optimal parameter values. To address these problems, the most recent filters have increased their level of complexity combining different strategies, so-called hybrid methods. In this study, two tools are proposed to improve the previous filters: a decimation tool for non-ground points and a densification process. Our main improvement is to combine these tools and a filter, in this case the Iterative Robust Interpolation Filter (IRI) (Kraus and Pfeifer in ISPRS J Photogramm Remote Sens 53(4):193–203. https://doi.org/10.1016/S0924-2716(98)00009-4 , http://www.sciencedirect.com/science/article/pii/S0924271698000094 , 1998), to (1) improve the filtering results in urban areas by removing buildings prior to filtering, which enables a downsizing of cells used for the selection of ground points and (2) to reduce the influence of parameters on the filtering accuracy. We used two LiDAR data sets: the reference data were acquired from the International Society of Photogrammetry and Remote Sensing (ISPRS) and the high density LiDAR data. In the first case, the results obtained are compared with those obtained in previous studies, using the metrics proposed by Sithole and Vosselman (ISPRS J Photogramm Remote Sens 59(1–2):85–101, https://doi.org/10.1016/j.isprsjprs.2004.05.004 , http://www.sciencedirect.com/science/article/pii/S0924271604000140 , 2004). For urban samples, the proposed hybrid method provided better results than the IRI algorithm, yielding a Kappa coefficient of 91.5%. The proposed method is one of the most accurate filters that has been tested with the ISPRS data. Finally, the results obtained on the basis of the high density LiDAR data reinforced the previous results and showed the potential usefulness of the proposed hybrid method. DecHPoints: Ein neues Werkzeug zur Verbesserung der Filterung von LiDAR-Daten in bebauten Gebieten. Die Identifizierung von Bodenpunkten in LiDAR-Daten bleibt auch über zwei Jahrzehnte nach der Entwicklung von ersten Filtermethoden eine Herausforderung. Der Erfolg der Filterung hängt sowohl von den physikalischen Eigenschaften der Umgebung als auch von der Qualität der vorliegenden Daten ab. Weitere Randbedingungen, die die Anwendbarkeit beeinflussen, sind die Wahl des Filters und seine optimale Parametrisierung. Um einer Lösung der Probleme näher zu kommen, sind heutige Filter aufwändiger konstruiert und nutzen dabei die Kombination unterschiedlicher Strategien, wenden also so genannte Hybridverfahren an. In dieser Studie werden zwei Verfahren zur Verbesserung bisheriger Filter vorgeschlagen: Eine Methode zur Ausdünnung zwecks Eliminierung von Nicht-Bodenpunkten und ein Verfahren zur anschließenden Verdichtung der ausgedünnten Punktwolke. Unsere wichtigste Neuerung ist die Kombination dieser Werkzeuge verbunden mit einem Filter, in unserem Fall auf Basis der iterativen robusten Interpolation (IRI) (Kraus und Pfeifer, 1998). Einerseits soll dadurch eine Verbesserung der Filter-Ergebnisse in bebauten Gebieten durch Eliminierung von Gebäuden vor der Filterung erzielt werden, die eine Verkleinerung der Zellen für die Auswahl der Bodenpunkte ermöglicht, andererseits wird dadurch der Einfluss der Parameterauswahl auf die Genauigkeit der Filterung vermindert. Zur Evaluierung wurden zwei LiDAR-Datensätze verwendet: Referenzdaten der Internationalen Gesellschaft für Photogrammetrie und Fernerkundung (ISPRS) und sehr dichte LiDAR-Daten für ein Testgebiet aus Spanien. Beim ersten Datensatz wurden die Ergebnisse mit jenen aus früheren Studien unter Verwendung der von den Organisatoren des Tests (Sithole und Vosselman, 2004) vorgeschlagenen Metriken verglichen. Bei den städtischen Testgebieten lieferte die vorgeschlagene Hybridmethode mit einem Kappa-Koeffizient von 91,5% bessere Ergebnisse als der IRI-Algorithmus. Die Genauigkeitsmaße der vorgeschlagenen Methode liegen unter den besten, die anhand der ISPRS-Daten erzielt wurden. Die Ergebnisse für den zweiten Datensatz bestätigen die bereits genannten Ergebnisse und zeigen die potenzielle Nützlichkeit der vorgeschlagenen hybriden Filtermethode.

[1]  Chuanfa Chen,et al.  A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data , 2013 .

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

[3]  Michael S. Renslow.  Manual of Airborne Topographic Lidar , 2013 .

[4]  Jorge García-Gutiérrez,et al.  Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities , 2013 .

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

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

[7]  Gunho Sohn,et al.  A model‐based approach for reconstructing a terrain surface from airborne LIDAR data , 2008 .

[8]  C. L. Miller.  The theory and application of the digital terrain model , 1958 .

[9]  Li-Der Chang,et al.  Bare-earth extraction and vehicle detection in forested terrain from airbone lidar point clounds , 2010 .

[10]  C. Hladik,et al.  Accuracy assessment and correction of a LIDAR-derived salt marsh digital elevation model , 2012 .

[11]  Chuanfa Chen,et al.  A robust method of thin plate spline and its application to DEM construction , 2012, Comput. Geosci..

[12]  James J. Little,et al.  Deforestation: Extracting 3D Bare-Earth Surface from Airborne LiDAR Data , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[13]  Wei Zhang,et al.  A Unified Framework for Street-View Panorama Stitching , 2016, Sensors.

[14]  Yong Li,et al.  An Improved Top-Hat Filter with Sloped Brim for Extracting Ground Points from Airborne Lidar Point Clouds , 2014, Remote. Sens..

[15]  R. Wack,et al.  DIGITAL TERRAIN MODELS FROM AIRBORNE LASER SCANNER DATA - A GRID BASED APPROACH , 2002 .

[16]  Sylvie Durrieu,et al.  A sequential iterative dual-filter for Lidar terrain modeling optimized for complex forested environments , 2012, Comput. Geosci..

[17]  Domen Mongus,et al.  Computationally Efficient Method for the Generation of a Digital Terrain Model From Airborne LiDAR Data Using Connected Operators , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Michael E. Hodgson,et al.  Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation , 2007 .

[19]  Liang-Chien Chen,et al.  Automated Searching of Ground Points from Airborne Lidar Data Using a Climbing and Sliding Method , 2008 .

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

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

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

[23]  M. Roggero.  Airborne laser scanning: clustering in raw data , 2001 .

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

[25]  Marco Heurich,et al.  Impact of Slope, Aspect, and Habitat-Type on LiDAR-Derived Digital Terrain Models in a Near Natural, Heterogeneous Temperate Forest , 2017, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

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

[27]  Wuming Zhang,et al.  An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation , 2016, Remote. Sens..

[28]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

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

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

[31]  Bingbo Gao,et al.  State-of-the-Art: DTM Generation Using Airborne LIDAR Data , 2017, Sensors.

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

[33]  N. Pfeifer,et al.  LiDAR Data Filtering and DTM Generation , 2017 .

[34]  N. Pfeifer,et al.  SEGMENTATION BASED ROBUST INTERPOLATION - A NEW APPROACH TO LASER DATA FILTERING , 2005 .

[35]  M. Brovelli,et al.  Managing and processing LIDAR data within GRASS , 2002 .

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

[37]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[38]  Y. Li.  FILTERING AIRBORNE LIDAR DATA BY AN IMPROVED MORPHOLOGICAL METHOD BASED ON MULTI-GRADIENT ANALYSIS , 2013 .

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

[40]  Wai Yeung Yan,et al.  Urban land cover classification using airborne LiDAR data: A review , 2015 .

[41]  Le Wang,et al.  A multi-resolution approach for filtering LiDAR altimetry data , 2006 .

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

[43]  M. Z. Rahman,et al.  ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM) WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION , 2015 .

[44]  I. Dowman,et al.  TERRAIN SURFACE RECONSTRUCTION BY THE USE OF TETRAHEDRON MODEL WITH THE MDL CRITERION , 2002 .