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EVALUATION OF LBTM FOR HRSI RECTIFICATION 
Sun Yushan 3 , Ahmed Shaker b , Wenzhong Shi c 
a Mapping from space, Key Laboratory of State Bureau of Surveying and Mapping , China Academy Surveying and 
Mapping 
b Survey Research Institute,National Water Research Center, Egypt, 
department of Land Surveying and Geo-Informatics, the Hong Kong Polytechnic University, Hong Kong, China 
. KEYWORDS: High-resolution satellite imagery, LBTM, linear feature, geometric rectification, affine, three-dimensional. 
ABSTRACT: 
At present, HRSI (High-resolution Satellite Imagery) is more and more widely applied for surveying, land, constructing, production 
and living field, and it becomes increasingly important to acquire orthophotograph by processing geometric rectification of satellite 
original images. Less or without GCPs (Ground Control Points) in several parts of area is a puzzled problem for remote sensing 
imagery rectification. In this paper a three-dimensional affine transformation, 3D affine LBTM (Line Based Transformation Model) 
is introduced, and is applied to achieve geometric rectification based on a set of artificial data which used to evaluate the feasibility 
of this developing model for different terrains and the number, density, elevation, slope, distribution and attitude requirement of 
linear features. 
1. INTRODUCTION 
At present, HRSI is becoming more and more extensively 
applied for urban planning, surveying, mapping, land 
management, agriculture and military field. Digital 
orthophotoimagery is the base of constructing basic service of 
national spatial data and digital earth. And it becomes 
increasingly important to acquire orthophotoimage by 
processing geometric rectification of satellite images. 
When we rectify the satellite image, the most important step is 
selection of ground control points. General speaking, control 
points should have the following characteristics: ground control 
points have obvious, clear position signs on the image; features 
on the GCPs do not variance along with time; selecting GCPs 
on the original image must base on the same terrain height; 
GCPs should well-proportioned distribute on the whole image; 
and should have quantitative ensure (Zhao, Y. S. (2003)). But 
in some unfrequented areas of China, especially Western China, 
there are not enough ground control points or control points are 
not well distributed on the image, and on the high-resolution 
remote sensing image, control points are not obvious and not 
easy to find out, so other methods need to be tried. 
Here, a new non-rigorous mathematics model, Line base 
transformation model is a suitable way to solve these problems. 
LBTM achieves image rectification base on line, such as river, 
road, bounding wall, slope, bank and ridge etc. if these line 
information also clear and well-proportioned distribute on the 
whole image, and do not variance along with time, these 
information can instead ground control points for image 
rectification in theory. This method developing, can change 
only use control point information for image rectification in 
current production, increase using rate of remote sensing 
image. 
After the principle introduction of this developing model, some 
set of three-dimensional coordinate data are assumed to 
analyze the feasibility of one of the familiar LBTM, three 
dimensional affine LBTM, which is used for three different 
terrains: flat terrain, hilly terrain and mountainous terrain. 
2. LBTM 
According to the principle analysis, practice and research for 
numbers of years (Shaker, A. and Shi, W. Z. (2003)), it is 
obvious that linear feature can be used rigorous mathematical 
models and points can be applied to non-rigorous mathematical 
models. That leads to the question of “Can linear features be 
used with non-rigorous mathematical models in order to 
circumvent the absence of satellite information and maintain 
satisfactory results?” The research by Shaker, A. and Shi, W. Z. 
(2004) answers the question with the development of a new 
model named the LBTM. 
With the LBTM, most of the problems of using linear features 
with rigorous models have been overcome. It is a very simple 
model which is time independent, can be applied to images 
from any linear array sensor, does not require any information 
about sensor calibration or satellite orbit, and does not require 
any initial approximation values. 
2.1 The Principle of LBTM 
The model can define the image transformation parameters 
either to use single linear features or to use linear features plus 
a number of control points. The basic principle of the model is 
that the relationship between line segments of straight lines on 
the image space and the object space can be expressed by 
conformal or affine transformation relationships (Shaker, A 
(2004)). 
Successful exploitation of linear features for image 
rectification and terrain modelling requires consideration of the 
following two major sides: the mathematical description of 
linear features in image and object space and the mathematical 
description of the relationship between the two spaces. There 
are different ways for representing linear features in image