Using empirical mode decomposition for ground filtering

LiDAR data provides valuable information for various remote sensing applications. For these, one important and challenging problem is ground filtering. This operation separates the bare earth and object data. Researchers proposed several methods to solve this problem. However, the complexity of the data limit the usability of these methods for all terrain types. Besides, the performance obtained in ground filtering should be improved further. In this study, we focus on this problem and propose a novel ground filtering method using Empirical Mode Decomposition (EMD). We tested the proposed method on the standard ISPRS data set and evaluate its strengths and weaknesses. We also compared the proposed method with the ones in the literature to show the improvements obtained.

[1]  Patrick Flandrin,et al.  Trend filtering via empirical mode decompositions , 2013, Comput. Stat. Data Anal..

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

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

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

[5]  Le Wang,et al.  Isprs Journal of Photogrammetry and Remote Sensing a Multi-directional Ground Filtering Algorithm for Airborne Lidar , 2022 .

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

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

[8]  J. Zhanga,et al.  DEM GENERATION FROM AIRBORNE LIDAR DATA , 2010 .

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

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

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

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

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

[14]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[15]  Zhaohua Wu,et al.  On the trend, detrending, and variability of nonlinear and nonstationary time series , 2007, Proceedings of the National Academy of Sciences.

[16]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

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