Modified Levenberg–Marquardt-Based Optimization Method for LiDAR Waveform Decomposition

A modified Levenberg-Marquardt (LM) method is proposed to improve the waveform-decomposition efficiency of light detection and ranging (LiDAR). The conventional constant-model-based LM fitting algorithm is subsequently modified using two proposed models: the linear model and exponential model. By revising the update coefficient of the damping term to make it consistent with the variation of residual error, the magnitude of oscillation is effectively reduced to provide better convergence. The models were experimentally verified using observed data acquired by our experimental large-footprint LiDAR system. The results indicate that the two modified LM-based algorithms provide better performance in terms of convergence speed and iteration efficiency for waveform decomposition in comparison with the traditional algorithm. Most prominently, the exponential LM algorithm provides 69% maximum improvement in convergence speed and 103% in acceptable iteration efficiency in comparison with the traditional algorithm.

[1]  R Chellappa,et al.  Simulation of error in optical radar range measurements. , 1997, Applied optics.

[2]  Dengxin Hua,et al.  Improvement of the signal to noise ratio of Lidar echo signal based on wavelet de-noising technique , 2013 .

[3]  Manolis I. A. Lourakis.  A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar , 2005 .

[4]  Duan Li,et al.  A novel full-waveform LiDAR echo decomposition method and simulation verification , 2014, 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings.

[5]  Bin Li,et al.  Stepwise decomposition and relative radiometric normalization for small footprint LiDAR waveform , 2011 .

[6]  J. Bryan Blair,et al.  Decomposition of laser altimeter waveforms , 2000, IEEE Trans. Geosci. Remote. Sens..

[7]  J. Blair,et al.  The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography , 1999 .

[8]  Mohammad Bagher Tavakoli,et al.  Modified Levenberg-Marquardt Method for Neural Networks Training , 2007 .

[9]  Wolfgang Wagner,et al.  WAVEFORM ANALYSIS TECHNIQUES IN AIRBORNE LASER SCANNING , 2007 .

[10]  Frédéric Bretar,et al.  Full-waveform topographic lidar : State-of-the-art , 2009 .

[11]  Zheng Niu,et al.  Range determination for generating point clouds from airborne small footprint LiDAR waveforms. , 2012, Optics express.

[12]  S. Durrieu,et al.  Advanced full-waveform lidar data echo detection: Assessing quality of derived terrain and tree height models in an alpine coniferous forest , 2009 .

[13]  Yifang Ban,et al.  Toward an Optimal Algorithm for LiDAR Waveform Decomposition , 2012, IEEE Geoscience and Remote Sensing Letters.

[14]  Asa Persson,et al.  VISUALIZATION AND ANALYSIS OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA , 2005 .

[15]  Lei Zhang,et al.  Improved empirical mode decomposition based denoising method for lidar signals , 2014 .

[16]  W. Wagner,et al.  Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner , 2006 .

[17]  I. Dowman,et al.  Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction * , 2007 .

[18]  William Puech,et al.  Processing full-waveform lidar data in an alpine coniferous forest: assessing terrain and tree height quality , 2009 .