MI
| eT
EC s onm am s NIC
Optical Receiver
ES oq
ref
Figure 1. The block diagram of two-wavelength lidar
The two-wavelength lidar system works as follows. The laser
emitter transmits the laser bean separately in red and near-
infrared range. The laser light will be reflected to detect objects
through the holophote composed of mirrors M1 and M2. Then
backscatter signals can be received by the Schmidt-Cassegrain
telescope with 200mm diameters, and be divided into two
wavelengths through dichroic filters D1. Subsequently, PMTI
and PMT2 are used to transform optical to electrical as the
devices of photo-electric detection. Finally data acquisition and
processing sub-system in the computer can obtain the back-
scatter intensities of the objects.
2.2 Noise analysis
After photo-electric detection, backscatter signals can be
acquired by the data acquisition sub-system of two-wavelength
lidar. There are two different signals respectively in red channel
and near infrared channel as shown in Figure 2.
near infrared |——
m in siis "m
as xe Tp Eg
Figure 2. Backscatter signals of two-wavelength lidar
It can be seen from the Figure 2 that the backscatter signals
received by the system are so bad that we cannot use directly for
further calculation. The possible noise and sources should be
analyzed before removal of noise.
Dark current, background radiation noise and circuit noise
caused by the detector and external environmental factors can
seriously affect the backscatter signals. The noise from the
sources is unrelated to each other in statistics. In the limited
bandwidth of the signal, the noise could be regarded as white
noise approximately and subject to normal distribution. Besides,
there will also be some colored noise due to the remaining
multimode interference effect of the laser beam. However, the
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
generated thermal noise and shot noise can be neglected
compared with dark current, background radiation noise and
circuit noise. Thus, noise of backscatter signals in two-
wavelength lidar system can be removed as the Gaussian white
noise
3. METHOD BASED ON WAVELET TRANSFORM
3.1 Principle of wavelet transform
Wavelet transform could map the signal to a group of basic
functions which were generated by the wavelet expansion and
shifting from. Thus the reasonable separation of signal could be
made both in different frequency bands and different moments
in the fact. Wavelet transform provides an effective tool for non-
stationary description of the dynamic signals and the extraction
of weak signal.
3.2 De-noising process
Traditionally, the de-nosing process based on wavelet transform
mainly consists of three steps: firstly, wavelet decomposition of
the leakage signals, for which we must select the best mother
wavelet and scale; secondly, threshold quantization of
decomposing coefficients was made; finally, reconstruction
wavelet de-noising signals was carried out.
Especially, based on the characteristics of the backscatter signals,
some of de-noising process in the two-wavelength lidar system
should be discussed in detail.
3.2.1 Wavelet decomposition
The wavelet transform is used to perform a multiscale
decomposition on the noisy signals. More specifically, the
corresponding wavelet coefficients are got by choosing suitable
wavelet and wavelet decomposition layers. Daubechies wavelet
function can effectively avoid the signal phase shifter and can
also achieve better smoothing effect in the process of
reconstruction on the basic of its symmetry and regularity
property. As a result, three orders Daubeachies wavelet was
chosen as the basic wavelet function in the de-noising process.
Besides, the noisy signal was decomposed to 4 layers.
3.2.2 Removal of the singular value
Through the analysis of characteristics of the backscatter signals,
there are many singular values caused by varieties of noise
sources in the signals. What's worse, the de-nosing result will be
seriously affected if the singular values can not be removed.
It is very convenient to distinguish the parts of noise and
saltation of signal by 3o-rule applied in electronic measurement
(HE Shi-biao, et al, 2002). The probability of the absolute value
greater than three standard deviations is only about 3%, and all
the values which are greater than 3o can be regarded as the
coarse errors. In this way, singular values could be removed
effectively.
3.2.3 Threshold quantification
In order to extract the wavelet coefficients of signals and remove
the wavelet coefficients of noise in the scales, a threshold should
be chosen to carry out the quantification from the first level to
the N layer of high frequency coefficients. In the real
applications, there are many methods to solve the problem.
