ERROR MODELLING ON REGISTRATION OF HIGH-RESOLUTION SATELLITE 
IMAGES AND VECTOR DATA 
: à "ja: a ^ t 
Pu-Huai Chen? *, Szu-Chi Hsu?^, Ge-Wen Lee" 
* Dept. of Surveying and Mapping Eng., Chung-Cheng Institute of Technology, Ta-Hsi, Taoyuan, 335 Taiwan, R.O.C. 
phchen@ccit.edu.tw 
® Dept. of Civil Eng., Chung-Cheng Institute of Technology, Ta-Hsi, Taoyuan, 335 Taiwan, R.O.C. 
Commission I, WG 1/4 
KEY WORDS: Geometric, Integration, Matching, Raster, Registration, Structure, Understanding, Vector 
ABSTRACT: 
Traditionally, image-and-map registration is carried out using low-level image processing techniques. One of inevitable problems 
resulted from a low-level image processing technique is the need to decide what the ultimately desired object is. An alternative 
way to register images and maps is to use a ‘top-down’ or high-level image understanding approach, for instance, a geometric- 
structure-matching (GSM) technique. The algorithm of the proposed GSM technique is validated using a Quickbird image and the 
corresponding cadastral map. The boundary lines and polygons of cadastral parcels are used as the elements of geometric structure 
in the studied case. An automatic technique has been developed to match image features and the corresponding vector data. In 
addition, prior knowledge about the error model in the procedures of image-and-map matching has not been fully understood, 
therefore, this paper also concentrates on the error model required to implement the algorithm and to achieve a high level of 
automation. The error model is vital to give a threshold for optimising the results of the proposed GSM technique. Preliminary 
results show that errors of the order of 5m from the procedures of image-and-map registration are possible, and that error is 
comparable with the predicted one. It is possible to eliminate the requirements of manual intervention for registering images and 
maps, provided that accurate vector data are available. Potential applications of the proposed algorithm include providing ground 
  
   
  
    
   
   
   
   
   
   
   
   
  
   
   
   
   
   
   
   
  
  
  
  
   
  
   
  
  
   
    
  
  
  
control for automatic photogrammetry and updating data of spatial information systems. 
1. INTRODUCTION 
High-resolution images taken by advanced sensors with ground 
sampling distance (GSD) on the order of less than 1m, such as 
Quickbird and Ikonos data, keep flowing in, and users of 
various fields demand reasonable solutions from 
photogrammetry and remote sensing community to cope with 
the needs of map revision and extraction of information 
promptly. Automation is always the main consideration for 
solving the above-mentioned requests. Many efforts have been 
made to understand and to extract information from images, 
and this kind of photogrammetric approaches can be called as 
forward solutions or ‘bottom-up’ approach. Unfortunately, the 
current methods for automatic extraction of information from 
satellite images are still far from practical. In general, the 
reason why visual brains of human beings are able to draw a 
map by using complex images is rather poorly understood, if 
not entirely unknown. This explains why the development of 
algorithms for automatic extraction of spatial information is 
progressing slowly (Sowmya and Trinder, 2001). 
Qn the contrary, currently available data, such as vector data, 
maps, and digital elevation models (DEMs), representing basic 
knowledge about areas of interest, have been proved useful for 
providing information of ground control for map revision and 
photogrammetric, or radargrammetric, tasks (Morgado and 
Dowman, 1997; Chen and Dowman, 2000; Habib and Kelley, 
2001). Comparing with traditional photogrammetric approach, 
  
* Corresponding author. 
this kind of operations might be called as reverse solutions 
or ‘top-down’ approach. Automatic image-and-map 
registration is still one of unsolved problems in pursuit of 
fully automatic photogrammetry, near-real-time map 
revision and smart spatial information systems (Dowman, 
1998; Heipke et. al., 2000). Traditionally, image-and-map 
registration is carried out using low-level image processing, 
or ‘bottom-up’, techniques. One of inevitable problems 
resulted from low-level image processing techniques is the 
need to decide what the ultimately desired object is. Since 
that the decision is made by a human operator after 
segmentation of features, obviously, the need of human 
interventions in the traditional processing procedures 
results in relatively low level of automation. An alternative 
way to register images and maps is to use a ‘top-down’ or 
‘high-level image understanding approach, provided that 
prior knowledge about image-and-map registration is 
available and applicable in automatic procedures (Shapiro 
and Stockman, 2001; Baltsavias, 2004). 
There is no intention in the paper to give a precise 
definition of knowledge, however, prior knowledge is 
referred to as any geo-spatial data or models available, such 
as roads, boundary lines and land parcels, which give 
geometric structure of areas of interest. Hence, the paper is 
aimed at using geometric structure defined by vector data, 
given by existing 2-D maps or spatial information systems, 
for image-and-map registration with a higher level of 
  
  
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