Full text: Mapping without the sun

A KIND OF THE METHODS FOR SAR AND OPTICAL IMAGES FUSION BASED ON 
THE LIFTING WAVELET 
Shao Yongshe a Chen Ying d Li Jing b 
a Survey Department of Tongji University, No. 1239 Si Ping Road, Shanghai, China, shaoysh@sina.com 
b Xi’an Research Institute of Survey and Mapping, Middle No.l YanTa Road, Xi’an, China, LiJing@sina.com 
KEY WORDS: SAR image, Optical image, Lifting scheme wavelet, Image fusion, Feature extraction 
ABSTRACT: 
Image fusion is an important kind of means improving on the targets detection and recognition in the monochromatic images. Firstly, 
the paper has researched how a right wavelet base can be chosen, and decomposes the SAR image and optical image using the lifting 
scheme wavelet. Then, the low-frequency images are integrated with the weighted average ways after the SAR low-frequency image 
is filtered using the regional average means. In order to integrate the high-frequency images well, a kind of the fusion methods that 
the windows can be changed according to the features for the noise reduction is present to improve the targets detection and 
discrimination. Finally, the fusion image is used to extract the features. The result shows that the fusion image is better than the 
original SAR image for feature extraction. 
1. INTRODUCTION 
In recent years, the technique of radar imaging made a rapidly 
development in China. Radar imaging has many outstanding 
merits including the capability to work in all-time and all 
weather, high spatial resolution and strong penetrability 
through the ground or vegetation etc. Therefore, radar imagery 
has been widely used in many fields. Differing from traditional 
ways to space remote sensing, radar imagery shows the special 
characteristics for the ground targets imaged. Speckle noise in 
radar imagery makes it difficult to extract the edge profile of 
targets using traditional ways. Integrating the SAR and optical 
images has been becoming a kind of primary ways for targets 
extraction and recognition. 
The image fusion technique, which can integrate the different 
sensors characteristics, is a kind of the ways to utilize the 
images of the different sensors effectively. Comparing to the 
traditional remote sensing modes, radar imaging possesses the 
capability to work in all-time and all weather, high spatial 
resolution and strong penetrability through the ground or 
vegetation, whereas the optical imaging expresses the spectrum 
information well. Accordingly, it is advantaged to integrate the 
SAR image and optical image for the targets detection and 
recognition Because of the different image theory and 
spectrum characteristic for SAR image and optical image, the 
traditional fusion methods, example for HIS, PCA and HPF etc., 
cannot make the satisfying results. The recent years, it has been 
shown that the wavelet transform is a good ways for the image 
fusion [7] . On the basis of having chosen an appropriate wavelet 
base, in order to make it more potent to integrate the SAR 
image and optical image, the paper focuses on the wavelet 
high-frequency sub-images and presents a kind of the fusion 
methods that the windows can be changed according to the 
features for the noise reduction. The result shows that the 
method may make the features keep better continuity in the 
fusion image than the SAR image, which improves the effect to 
detect and recognize the targets. 
2. THE WAVELET BASE CHOICE FOR THE IMAGE 
FUSION 
The wavelet base makes an important influence on the image 
fusion [2][3] . The different wavelet bases demonstrate the unlike 
characteristics that include the orthogonality, symmetry, filter 
length, the tighten support length and the vanishing moments. 
The wavelet coefficients, which relate to the wavelet base, are 
the foundation of the image fusion based on the wavelet 
transform. Therefore, the different wavelet bases may make the 
diverse results for the image fusion. 
Orthogonality presents the integrality of the wavelet base, and 
simplifies the calculation process. Haar wavelet shows the trait 
of the linear phase and can reconstruct the image completely, 
whereas others don’t. The tighten support characteristic 
presents the localization of the wavelet base. The wavelet that 
doesn’t reveal the tighten support trait will lead to the error of 
the truncation when the wavelet is used to decompose and 
reconstruct the image. The vanishing moment determines the 
astringency of the wavelet function. For the image embodying 
the abundant texture, the great vanishing moment will 
transform the great wavelet coefficients more. On the contrary, 
it will transform the small wavelet coefficients more. The 
greater the vanishing moment is, the longer the filter, which 
increases the operand. Symmetry shows the linear phase, and 
reconstructs the image’s edge well. Symmetrical wavelet base 
can obtain the better visual effect because human isn’t sensitive 
to the error of the symmetry.
	        
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