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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 
A RECOGNITION METHOD FOR AIRPLANE TARGETS 
USING 3D POINT CLOUD DATA 
Mei Zhou*, Ling-li Tang, Chuan-rong Li, Zhi Peng, Jing-mei Li 
Academy of Opto-Electronics, Chinese Academy of Sciences, No.9, Dengzhuang Nanlu, Beijing, China 
zhoumei@aoe.ac.cn 
Commission III, WG III /2 
KEY WORDS: LiDAR, Point cloud data, Target recognition, Target segmentation, KD-Tree, Moment invariants 
ABSTRACT: 
LiDAR is capable of obtaining three dimension coordinates of the terrain and targets directly and is widely applied in digital city, 
emergent disaster mitigation and environment monitoring. Especially because of its ability of penetrating the low density vegetation 
and canopy, LiDAR technique has superior advantages in hidden and camouflaged targets detection and recognition. Based on the 
multi-echo data of LiDAR, and combining the invariant moment theory, this paper presents a recognition method for classic 
airplanes (even hidden targets mainly under the cover of canopy) using KD-Tree segmented point cloud data. The proposed 
algorithm firstly uses KD-tree to organize and manage point cloud data, and makes use of the clustering method to segment objects, 
and then the prior knowledge and invariant recognition moment are utilized to recognise airplanes. The outcomes of this test verified 
the practicality and feasibility of the method derived in this paper. And these could be applied in target measuring and modelling of 
subsequent data processing. 
1. INTRODUCTION 
LiDAR (Light Detection and Ranging) is an active remote 
sensing system which can quickly provide three dimensional 
information of earth surface and object. Currently it has been 
used in many fields, such as 3D city models, urban planning, 
design of telecommunication networks, vegetation monitoring 
and disaster management, etc. Based on the characteristic of 
LiDAR that can penetrate vegetation cover, this paper in 
particular focuses on the research on the method of target 
extraction by using multi-echo point cloud data of LiDAR. 
Considering the application of three-dimensional target 
recognition using LiDAR data, it mainly focuses on airplane, 
building, power lines, etc. So far many methods were proposed 
but there are still some problems. The first one is about how to 
express and recognize objects of arbitrary shape while most of 
the recognition methods restrict the objects’ shape at present. 
Secondly, objects are usually in complex background while 
many methods can only apply to a single object without 
considering surrounding. Thirdly, it’s difficult to recognize 
object with uncompleted information. Due to the limitation of 
the current equipment, the distance between point clouds is 
fairly long, so we could only get information on large objects 
without details. Considering there are many kinds of object with 
a complex background in one area, artificial interpretation 
would cost a lot of time and work. So, it’s necessary to design 
reasonable algorithm on targets recognition. 
Currently, bare objects orientated recognition and abstraction 
based on LiDAR point data mainly focus on building, road and 
power line abstraction. A relative smaller body of literatures 
address disguised objects abstraction, also mainly concentrating 
on camouflage net disguised objects detecting (Marino et al., 
2005; Buck et al., 2007). In general, most of those recognition 
  
* Corresponding author. 
methods are based on region, outline or feature points 
(Prokhorov, 2009; Golovinskiy et al., 2009). Considering the 
airplane targets in LiDAR point cloud data, the direction of 
airplanes may be arbitrary, even the outline or size is different, 
while their shapes have certain similarities, so the method must 
consider both deformation and scale invariance. The moment 
invariants are suitable to solve the problem mentioned above. 
Automatic recognition of aircrafts based on moment invariants 
from binary television image was described (Sahibsingh et al., 
1977). In this paper, the profile and boundary of targets was 
extracted from binary television images before the moment 
transformation, and then classification experiments were carried 
out base on a Bayes decision rule and a distance-weighted k- 
NN rule. Zhongliang et al. (1992) proposed a new method for 
automatic ship classification using superstructure moment 
invariants. In those papers, the classification methods of 
aircrafts or ships extracted from images by moment could be 
also applied on LiDAR point cloud data, but point cloud data is 
discrete, there are some differences between the distance image 
and binary image, especially in the detail of the target outline. 
Hans-Gerd et al. (1999) worked on invariant moments in raw 
laser altimetry data to extract model parameters of standard 
gable roof type houses, such as location, length, width and 
height of a building as well as its orientation, roof type and roof 
slope. Jinhui et al. (2009) proposed a new approach of target 
recognition based on LiDAR point cloud data by affine 
invariable moment, the experiment was based on data acquired 
by terrestrial laser scanner. Firstly the target images were 
generated by distance value of point cloud data, and then 
through the process of filtering, thresholding and region 
labelling, the affine moments of target region were extracted as 
features, finally BP network and SVM algorithm were used for 
target classification and recognition. There would be still