Volume XL-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 393-397, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W1-393-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 393-397, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W1-393-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 May 2013

02 May 2013

ENHANCED COMPONENT DETECTION ALGORITHM OF FULL-WAVEFORM LIDAR DATA

M. Zhou, M. H. Liu, Z. Zhang, and J. H. Wang M. Zhou et al.
  • Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing, China

Keywords: Full-waveform LiDAR, Component detection, Waveform decomposition, LM, FMM

Abstract. When full-waveform LiDAR (FW-LiDAR) data are applied to extract the component feature information of interest targets, there exist a problem of components lost during the waveform decomposition procedure, which severely constrains the performance of subsequent targets information extraction. Focusing on the problem above, an enhance component detection algorithm, which combines Finite Mixed Method (FMM), Levenberg-Marquardt (LM) algorithm and Penalized Minimum Matching Distance (PMMD),is proposed in this paper. All of the algorithms for parameters initialization, waveform decomposition and missing component detection have been improved, which greatly increase the precision of component detection, and guarantee the precision of waveform decomposition that could help the weak information extraction of interest targets. The effectiveness of this method is verified by the experimental results of simulation and measured data.