Full text: Mapping without the sun

LIN Yi a , JIAN Jianfeng b , ZHANG Shaoming 3 , XIE Feng 3 
3 Department of Surveying and Geoinformatics, Tongji Univ., Shanghai 200092, China 
b Ministry of Edu. Key Lab. of Computer Network and Information Security, Xidian Univ., Xi’an 710071, China 
c Xi’an Research Institute of Mapping and Surveying, Xi’an 710054, China 
Tel: 021-65986960, Fax: 021-65985811, Email:linyi@mail.tongji.edu.cn 
KEY WORDS: remote sensing images, registration, feature point, variant moment, relaxation matching 
An optimization method that registering the remote sensing images which are from different sensors, with different resolution and 
taken at different time, is presented in this paper. First the feature points are quickly extracted with the operators of gradient and 
Forstner, and evenly distribution by the use of the grid technique based on the entropy. With the help of some exact control points, 
Based on the matching rule of the variant moment similarity measurement and the matching strategy of the global relaxation, the 
conjugated points are gotten by quickly registering the remote sensing images. Finally, the error points are eliminated by using the 
quadratic polynomial model. Experiment results show, the method, with the quickly registering speed and the high accurate and 
evenly distributing conjugated points, can meet the need of the image fusion and quickly updating of the remote sensing images. 
With the development of modem remote sensing technology, 
remote sensing is growing up to multi-sensor , multi 
resolution, multi-spectrum(ultra-spectrum) and multi-temporal 
information acquisition and fast intellectualized processing. The 
amount of data of remote sensing is rapid rise, how to dig out 
the potential of mass data and to improve the efficient of dada 
application is a new task on the technology development of 
remote sensing image processing. Earth observation satellite 
has provided more and more multi-space resolution, multi 
temporal and multi-spectrum image in the same place. 
Moreover, it has provided abundant data for hypsometry, map 
updating, the classification of land resource utilization, crops 
classification and forest classification , flood disaster 
monitoring, variety monitoring in ecological resource, etc.. 
During the transition from the applied analysis of single sensor 
image to the analysis and application of multi-spectrum, multi 
sensor , multi-platform , multi-temporal , multi-resolution 
image, automatic registration and merging of multi-source 
remote sensing information is very important to realize the 
spatial data variety detection and image date updating, 
moreover, spatial registration of multi-source image the very 
important step of the next image merging, its error direct 
influence the result validity of multi-source image merging. 
The traditional image registration method is to manual seek the 
homologous between the undetermined registration image, it is 
very time-consuming and arduous, and could not adapt the 
demand of mass of data processing, furthermore, it has a great 
subjective influence over the precision of registration. 
Therefore, many researchers go out their way to look for the 
automatic or semi-automatic image registration. Strunz and 
other scientists have put forward a schema of automatic seeking 
control point for image geometry rectification; Diamdji has 
proposed a automatic registration method of different resolution 
image based on wavelet transform. The both proposals take the 
low-resolution image as reference image and lose the high 
resolution information. Professor Zhang Zuxun has put forward 
a full automatic remote sensing image registration method 
based on probabilistic relaxation total matching of multistage 
image, it could use in different temporal and resolution image 
registration, but this method didn’t consider the uniform 
distribution of matching points and how to reject the 
mismatching points; Zhang Jixian has proposed a fast automatic 
image registration method by using polynomial model to total 
rectification , feature extraction combined with pyramid 
template matching. But the multilayer template matching just 
used some local information around the undetermined 
registering point, and it couldn’t use the strong correlation 
between undetermined registering point and registered point, 
moreover, it didn’t consider the global consistency of result, 
and it is lack of registering reliability. 
In this paper, a registration method of multi-resource remote 
sensing image is presented. The basic thought is to fast extract 
the feature points with the operators of Roberts gradient and 
Forstner, at mean time, to control the evenly distribute by using 
of the grid technique based on the entropy. With the initial 
steering by a few of exact control points, the homologous are 
gotten by quickly registering the remote sensing images, based 
on the matching rule of the variant moments similarity 
measurement and the matching strategy of the global relaxation. 
Finally, the error points are eliminated by using the quadratic 
polynomial model. Experiment results show that the method, 
with the quickly registering speed and the high accurate, can get 
the evenly distributing homologous and meet the need of the 
quickly updating the remote sensing images. 
There are some ordinary operator for feature points extraction 
such as Moravec one and Forstner one[14], etc.. Moravec 
operator is more convenient than Forstner one, however 
Forstner operator has better precision and could indicate the 
type of feature points. In this paper, we prefer Forstner one 
because of its better precision. Before extracting the feature

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