A voxel-based multiscale morphological airborne lidar filtering algorithm for digital elevation models for forest regions

Abstract In this paper, we proposed a voxel-based morphological filtering algorithm that can generate very accurate digital elevation models (DEMs) over forest regions because accurate DEMs are essential for forest mapping and other forest applications. Height distribution analysis, convexity constraints, morphological filtering, and moving-window voxel-based filters are exploited to detect object points. An object index is introduced and is computed by Otsu segmentation to label the classified lidar points i.e. indices above the threshold for objects are regarded as objects. To validate the proposed algorithm, multiple experiments, including the ISPRS benchmark datasets and ten forest datasets, are conducted and compared with several existing lidar filtering algorithms. The test results of the ISPRS datasets indicate that the proposed algorithm achieved low commission errors ranging from 1.53% to 6.91%. Also, the test results of the forest datasets demonstrate that the mean errors of the proposed algorithm are compatible with those from other algorithms.

[1]  Chuanfa Chen,et al.  A fast and robust interpolation filter for airborne lidar point clouds , 2017, PloS one.

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

[3]  Xiaohuan Xi,et al.  A revised progressive TIN densification for filtering airborne LiDAR data , 2017 .

[4]  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.

[5]  James J. Little,et al.  A Hybrid Conditional Random Field for Estimating the Underlying Ground Surface From Airborne LiDAR Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  F. Crosilla,et al.  LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories , 2013 .

[7]  F. Pukelsheim.  The Three Sigma Rule , 1994 .

[8]  Yanjun Su,et al.  Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas , 2016 .

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

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

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

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

[13]  J. Shan,et al.  Urban DEM generation from raw lidar data: A labeling algorithm and its performance , 2005 .

[14]  Norbert Pfeifer,et al.  New Associate Editor pp iii-iv Segmentation of airborne laser scanning data using a slope adaptive neighborhood , 2006 .

[15]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[16]  Mohammad Javad Valadan Zoej,et al.  A Novel Filtering Algorithm for Bare-Earth Extraction From Airborne Laser Scanning Data Using an Artificial Neural Network , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

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

[19]  Domen Mongus,et al.  Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces , 2014 .

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

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

[22]  A. Habib,et al.  Photogrammetric and Lidar Data Registration Using Linear Features , 2005 .

[23]  Cem Ünsalan,et al.  LiDAR Data Filtering and DTM Generation Using Empirical Mode Decomposition , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.