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DATA FUSION WITH INTEGRATION OF AIRBORNE LASER SCANNING DATA AND 
ORTHO-AERIAL PHOTOS 
Hangbin Wu a * ChunLiu a,b Xinhua Zhou c 
a Department.of Survey and Geo-Informatics, Tongji University, Shanghai, China -wuhangbin_csdn@yahoo.com.cn, 
liuchun@mail. tongj i. edu.cn 
b Key Laboratory of Advanced Engineering Surveying of SBSM, Shanghai, China-liuchun@mail.tongji.edu.cn 
c The First Institute of Oceanography, SOA, Qingdao, China- xhzhou@fio.org.cn 
Commission I, WG 1/2 
KEY WORDS: Fusion, interpolation, IHS transform, Principal Components Analysis, Vector Fusion 
ABSTRACT: 
Two kinds of method, raster fusion and vector fusion, is proposed for fusion operation by integration of LIDAR point cloud and 
Ortho aerial photos in this paper. The data set used in the paper and data process for preparation is introduced firstly. In order to 
complement the raster fusion, the points cloud is interpolated into a raster style by Inverse Distance Weighted(IDW) method using Z 
and Intensity values. Then the IHS transform and Principal components analysis(PCA) algorithms are used to fuse the data 
information. For vector fusion, the overlay analysis, which is a technology of spatial analysis, is chosen to integrate the spectral 
information with points. The quantitative analysis about entropy is conducted to evaluate the fusion results. It shows that the PCA 
fusion is better than IHS transform. 
1. INTRODUCTION 
1.1 Objective 
Airborne laser scanning system (LIDAR) is an advanced active 
sensing system on acquirement of the ground three- 
dimensional data. The system emits a controlled laser radiation, 
independent of solar light, to have the ability to observe the 
target on the ground day and night. It can obtain the ground 
three-dimensional data directly, with higher precision, higher 
efficiency, higher density and lower cost than traditional 
measuring methods, which is the forefront of photogrammetry 
and remote sensing area. 
Since the 1980s, airborne laser scanning technology had a 
major breakthrough, Germany, the Netherlands, the United 
States, Canada, and other related research institutions are 
paying much attention on laser scanning altimetry and 
extraction of topographical features. The technologies had been 
flourishing since the end of the 20th century, gradually 
expanding the scope of application. 
The current airborne laser scanning technology developed 
rapidly, which has been able to record location information by 
calculating several times echo and echo intensity information, 
and provide the same region of digital photos and wave data. 
On the other hand, compared to the current airborne laser 
altimeter scanning hardware development, data-processing 
algorithm has lagged behind. The method depending on the 
altimeter data to extract feature needs quite improve, especially 
in the data reliability and accuracy fields. If the integrated 
image data, multi-spectral data and GIS data can complement 
each other and make full use of their advantages that is 
expected to achieve a satisfactory result. 
This paper analyses the two expression forms of the LIDAR 
point cloud, as well as the interpolation of point cloud: based 
on the integration of grid and based on the integration of vector. 
Then the point cloud data are integrated with Ortho-photomap, 
according to Z coordinate values and echo intensity. The 
process based on grid integration, uses the transform HIS 
methods and PCA methods. And the process based on vector 
integration uses GIS Spatial Analysis methods. 
1.2 Existing relative works 
Fusion of remote sensing images is an advanced image process 
technology to inosculate the information from different kinds 
of data sources. The main purpose of this technology is to 
integrated the different spectral information from certain sensor 
or different sensors, eliminatethe redundancy and contradictory, 
reduce the ambiguility, enhance the transparency and improve 
the accuracy and reliability of image interpretation. Besides, 
this technology is superior in these fields(LI Jun, ZHOU 
Yue-Qin and LI DeRen,1999): ©Sharpen image; 
©Improve the accuracy of geometric correction; ©Provide the 
ability of stereo measurement based on photogrammetry; 
©Increase the feature information from single data source; 
©Improve the results of classification; ©Change detection 
from multitemporal data;© Replace the missed information by 
other data sources; ©Overcome the imperfection of objects 
extraction and reorganization. 
Researches for images fusion between remote sensing images 
have been started for many years. The most traditional methods 
are IHS transfer(Yang Jin and Liu Jianbo,2007), Principle 
Component Analysis (PCA)( WANG Wenwu, 2007), High 
Pass Filter (HPF) and so on. Wavelet fusion is also common 
used and developed recently. It is mainly focused on the fusion 
* Corresponding Author.