A Wavelet-Based Echo Detector for Waveform LiDAR Data

• Published in: IEEE transactions on geoscience and remote sensing 2016-02, Vol.54 (2), p.757-769
• Main Authors: Wang, Cheng-Kai, Tseng, Yi-Hsing, Wang, Chi-Kuei
• Format: Article
• Language: English
• Subjects: Algorithms; Computer simulation; Datasets; Dealing; Detector; Detectors; digital elevation model (DEM); echo; Laser radar; Lidar; light detection and ranging (LiDAR); Noise; Sensors; Signal processing algorithms; Surface waves; Three dimensional models; Three-dimensional displays; Transformations; waveform; Waveforms; wavelet; Wavelet transforms
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2015.2465148

This paper presents a wavelet-based (WB) echo detector that can recover the echoes missed by a light detection and ranging (LiDAR) system via on-the-fly detection. An on-the-fly detection method normally utilizes a simple threshold (TH) to register a target point. Points that belong to weak and/or overlapping echoes are much complicated and are easily missed by TH approaches. The proposed detector based on wavelet transformation is robust to noise and is capable of resolving overlapping echoes. It is thus expected to be good at handling missing echoes. A simulated waveform data set and a real waveform data set of a forest area were both used in this paper. The simulated waveform data were utilized to compare the proposed detector with zero crossing (ZC) and Gaussian decomposition (GD) detectors in terms of their ability to deal with weak or overlapping echoes. The real waveform data set acquired from Leica ALS60 was used to demonstrate a WB algorithm for exploring the missing echoes. Experiments using the simulated data showed that the WB and GD detectors are superior to the ZC detector in finding overlapping echoes. The WB algorithm performs well when dealing with overlapping echoes with a low signal-to-noise ratio. Experiments using the real waveform data show that 31.5% additional weak or overlapping echoes can be detected by the WB detector compared with the point cloud provided by the system. With such additional points, the mean and root-mean-square errors of the digital elevation model differences can be improved from 0.72 and 0.79 m to 0.16 and 0.59 m, respectively.