signal itself. Through a sifting process described by Huang et 
al., the signal can be decomposed into a series of intrinsic 
mode functions (IMF) and the residual through the sifting 
process. 
V, owed (r) = S IMF, (r) + R, (r) (2) 
jl 
Where IMF - a series of intrinsic mode functions 
R, = residual 
Each IMF satisfies two conditions: the number of extrema and 
the number of zero crossings must either be equal or differ at 
most by one, and the mean of the upper and lower envelopes 
derived from local extrema is zero at any point. This allows for 
physically meaningful instantaneous frequency and amplitude 
calculation through the Hilbert transform performed on the 
IMFs. And any IMF represents a simple oscillatory mode. The 
low-order IMFs represent high frequency oscillation 
components, while high-order IMFs represent low frequency 
oscillation components. The noise and signal are traditionally 
characterized by high frequency and low frequency, 
respectively, so they can be distinguished by the empirical 
mode decomposition method. While the EMD technique has 
been applied to various fields, although the theoretical base is 
empirical, some research has shown that the EMD-based signal 
denoising method is effective in the analysis of a lidar signal . 
2.2 Ensemble empirical mode decomposition 
The empirical mode decomposition results sometimes become 
invalid because of mode mixing, which is defined as either a 
single IMF consisting of more oscillatory modes, or an 
oscillatory mode residing in different IMF. 
To overcome the phenomena of mode mixing, the ensemble 
empirical mode decomposition (EEMD) method has been 
proposed. This method is a noise assisted data analysis method. 
The It repeatedly decomposes the signal into IMFs by using the 
EMD method. During each trial of the decomposition process, 
white noise is added to the original signal. The final results are 
obtained as the mean of corresponding IMFs of the 
The mode mixing can be effectively eliminated by the EEMD 
process. 
2.3 EEMD-based denoising method 
The EEMD-based signal denoising method is achieved as 
follows. 
Step 1: Decompose the signal with the EEMD method. At last 
the original signal is decomposed into a series of IMFs and a 
trend. 
Step 2: Reconstruction. In the time domain, the lower order 
and higher order IMFs represent the fine scales and coarse 
scales, respectively. It is assumed that low-order IMFs contain 
little value of the backscattering signal, and the denoising 
method is performed by obtaining the residual with the 
removal of some low-order IMFs. 
    
    
    
   
  
  
  
      
   
  
   
     
     
     
   
   
    
   
    
    
   
    
  
    
    
  
   
   
     
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
3. DATA 
3.1 The dual field-of-view lidar 
A dual field-of-view lidar (DFL) system was developed by the 
State Key Laboratory of Information Engineering in Surveying, 
Mapping and Remote Sensing (LIESMARS), Wuhan 
University. The lidar has two independent receiving channels 
to solve the problem of the dynamic range of lidar. The field- 
of-view of the near-range and far-range channels is 10 and 1 
mrad, respectively. The laser beam fully enters the field-of- 
view of the near-range and far-range channels from a distance 
of about 360 m and 1000 m, respectively. 
  
  
Telescope 
10" 
Telescope 
8" 
  
  
  
  
  
  
    
  
Display 
Filter Zt 
[| 
P 
M 
T 
  
  
Data acquisition 
system 
  
  
  
Fig. 1. The schematic diagram of the DFL system. 
3.2 data 
Three pairs data are shown in Fig. 2. They are obtained by the 
dual field-of-view lidar at 20:10 and 20:12 Aug. 3, and 01:40 
Aug. 5. 
The comparison of these two simultaneous signals within the 
range from 1.5km to 3.0km indicates similar useful signals and 
obviously different noise. The signals obtained from the two 
channels are similar because of simultaneous measurements 
and the same altitude of the atmosphere, and this is evidenced 
by long-term observing data. The noise intensity of the near- 
range channel is higher because of the large field-of-view and 
low efficiency of the optics and electronics. First, the larger 
field-of-view means more interference from background light. 
Second, the signal of the near-range channel is restricted to 
avoid saturation of the receiver. 
  
  
  
  
  
  
  
  
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