; the 
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ation 
rmed 
The 
13) 
14) 
15) 
jt in 
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“The 
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jared 
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through the first order condition for stationary points of (16). 
The stationary points of (11) can be found by means of 
Lagrange multipliers, which is given by 
BE CN. BA C 
$E Y Gyr e s anm 
i=l k=1 k=1 izl 
Setting the gradients of J, with respect toy , y and 4 equal to 
0, when p? »0 and; »1,theU , V would minimize only if 
nee IxisC,IskSsN.(9) 
j=l D, 
And 
Sx, 1<i<C(20) 
2 
i 
up N 
s Qu)" 
FA 
Thus, we could simply apply the fuzzy C-means algorithm, by 
performing the iteration of (19) and (20). 
4. CLASSIFICATION 
Two different approaches were taken to perform the 
classification ~~ process, supervised and unsupervised 
classification. 
4.1 Supervised classification 
In the study area, the data has 538299 points and has a density 
of 1.2points/m?. Five typical types of objects, bare land, trees, 
houses, road and farm land, were selected and formed the 
training data and their properties were computed. 
   
Figure 1: supervised point cloud classification result 
The classification result is presented as in figure 1. The results 
shows that by the addition properties, that are the width, 
amplitude and cross-section of the backscattered waveform, 
objects of the same height but has different width and cross- 
section could easily distinguished. Also, object in 
neighbourhood spaces could also be separated by components’ 
intensity and width parameters and research an excellent 
classification performance. However, because of the correlation 
among the parameters, which draws back the classification 
results. 
4.2 Unsupervised classification 
  
Figure 2. Unsupervised classification results 
The correlation among the parameters was removed by the 
transform and the parameters were applied to the point cloud 
classification using Fuzzy C-Means algorithm. The 
classification result is shown in figure 2, which shows that low 
vegetation point could be classified and improve the 
classification performance. 
5. CONCLUSION 
The experiments in this paper shows that by using the additional 
parameters abstracted from full-waveform LiDAR, supervised 
classification approach could research good classification 
performance. Also, through IHSL transformation of the 
parameters, then the fuzzy C-means algorithm is applied to the 
derived new space to complete the LiDAR point classification 
procedure. By comparing the two different segmentation results, 
which may of substantial importance for further targets 
detection and identification, a brief discussion and conclusion 
were brought out for further research and study. 
6. REFERENCES 
A. Charaniya, R. Manduchi, and S. Lodha, "Supervised 
parametric classification of aerial lidar data," in Real-Time 
3D Sensors and their use workshop, in conjunction with 
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C. Frueh, S. Jain, and A. Zakhor, “Data Processing Algorithms 
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C. Mallet and F. Bretar, “Full-waveform topographic lidar: 
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Remote Sensing, vol. 64, no. 1, pp. 1-16, 2009. 
H. Gross, B. Jutzi, and U. Thoennessen, “Segmentation of tree 
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K.R. Castleman, Digital Image Processing. Prentice Hall, 
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