International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
Figure 3. Result of airplane recognition 
In order to demonstrate the precision and robustness of the 
proposed method, another LiDAR point cloud data is shown in 
Figure 4 (the red and green points respectively represent ground 
and non-ground points), which contain 64954 points and with a 
density of 1.7 points/m?, and the results are shown in Figure 5 
(the green and red points respectively represent airplane target 
and other target points). 
         
  
   
Figure 4. Another point cloud data to be identified 
Figure 5. Result of airplane recognition 
In this paper, we firstly use KD-tree to organize and manage 
point cloud data, and make use of the clustering method to 
segment objects, and then the prior knowledge and invariant 
recognition moment are utilized to recognise airplanes. Some of 
depth images obtained from results of the clustering method are 
shown in Figure 6 (for displaying, in the depth images, the grey 
range is 127-255, and the points of no value are set to 0). The 
results in top row are correct, while the ones in below row are 
incorrect, but can be eliminated by the invariant recognition 
moment. 
Figure 6. Depth images of the clustering results 
       
In addition, in order to verify the effectivity of moment 
invariants, we carried out an experiment utilizing the match 
template method (Bin, 2008) to recognize airplanes in the same 
depth image. Table 1 lists the performance comparison. The 
results show that the moment invariants used in this paper is 
superior to the other. 
  
  
  
  
  
  
  
  
  
  
  
Result of Result of 
this paper | paper (Bin, 
2008) 
Total number of targets 16 16 
Number of correct results 15 13 
Number of incorrect results 1 4 
Number of miss targets 1 3 
Accuracy rate (%) 93.75 81.25 
False alarm rate (%) 6.25 23.53 
  
Table 1. Performance comparison 
6. CONCLUSIONS 
Airplane recognition based on LiDAR point cloud data is a 
brand new application domain. Taking advantage of KD-tree 
and Moment Invariants, this paper presents a novel method to 
recognize airplane targets. And by carrying out tests we 
validated its feasibility and practicality. Considering many 
other factors, e.g. canopy density, canopy thickness, and 
LiDAR hardware properties, could influence the effect of 
disguised objects detecting by using point cloud data, the 
further research work should be considered the above issues. 
In addition, the approach of this paper could also be applied to 
other kinds of targets (even hidden targets) recognition. And we 
are now working on the algorithm evaluation and perfection, as 
well as analyzing the factors that affect targets recognition and 
researching the better method for neighbour targets 
segmentation. 
7. REFERENCES 
Bin, X., 2008. Research on object extraction and measurement 
based on LiDAR data, Master Thesis, Academy of Opto- 
Electronics, Chinese Academy of Sciences, Beijing, China. 
Buck, J., Malm, A., Zakel, A., Krause, B., Tiemann, B., 2007. 
High-resolution 3D coherent laser radar imaging. Laser 
Radar Technology and Applications XII. Proc. of SPIE, 
Vol. 6550, pp. 655002. 
Golovinskiy, A., Kim, V., Funkhouser, T., 2009. Shape-based 
recognition of 3D point clouds in urban environments.