1141 
STUDY OF REMOTE SENSING IMAGE FUSION AND ITS APPLICATION IN IMAGE 
CLASSIFICATION 
Wu Wenbo,Yao Jing*,Kang Tingjun 
School Of Geomatics,Liaoning Technical University, 123000, Zhonghua street,Fuxin,China - yaojingl 124@163.com 
Commission VII, WG VII /6 
KEY WORDS: Landsat image, data fusion, spectral Distortion, Classification accuracy, Algorithms evaluation 
ABSTRACT: 
Data fusion is a formal framework in which is expressed means and tools for the alliance of data originating from different sources. 
It aims at obtaining information of greater quality; the exact definition of ‘greater quality’ will depend upon the application. 
Satellites remote sensing image fusion has been a hot research topic of remote sensing image processing. Make Multispectral images 
matching with TM panchromatic image, and the error control in 0.3 pixels within. Use Smoothing Filter-based Intensity Modulation 
(SFIM), High Pass Filter (HPF), Modified Brovery, Multiplication, IHS, Principle component analysis Transform (PCA) methods 
for the fusion experiment. Use some parameters to evaluate the quality of fused images. Select representative features from the fused 
and original images and analysis the impact of fusion method. The result reveals that all the six methods have spectral distortion, 
HPF and SFIM are the best two in retaining spectral information of original images, but the PCA is the worst. In the process of 
remote sensing image data fusion, different method has different impact on the fused images. Use supervised classification and 
unsupervised classification method to make image classification experiments, the study reveals that all the fused images have higher 
spatial frequency information than the original images, and SFIM transform is the best method in retaining spectral information of 
original image. 
1. INTRODUCTION 
The specific objectives of image fusion are to improve the 
spatial resolution, improve the geometric precision, enhanced 
the capabilities of features display, improve classification 
accuracy, enhance the capability of the change detection and 
replace or repair the defect of image data [1] . 
But for a long time, remote sensing image fusion is mainly used 
to enhance the visual interpretation, and it not usually used in 
the research of improving the image classification, the main 
reasons are shown as follows [2] : (D Image fusion is mostly 
based on the fusion of different satellite. Because of the 
difference of the various parameters and phase between 
different sensors, as well as the inevitably registration error, led 
to the fusion classification results unsatisfactory;© Although 
the same sensor system provided different spatial resolution 
images, because of its low spatial resolution, resulting in poor 
classification effect; ©Because of the unreasonable fusion 
algorithm or classification method make the failure of 
classification. 
In this paper, using Landsat ETM + images panchromatic bands 
and multi-spectral bands to fuse, to research the fusion 
technology of different spatial resolution based on the same 
sensor system and the classification technology, evaluate the 
infection of each fusion method with the land use classification. 
2. THE CHOICE OF DATA SOURCES AND 
SUMMARIZE OF THE PRETREATMENT 
In this paper, using the image data of Landsat 7 ETM + 
panchromatic and multispectral images of August 2001,the 
study area is Shenyang.There are many types of feature in this 
area ,the main features include rice, dry land, forest, water 
bodies, residents of villages and towns and so on. 
2.1 Bands Selection 
Bands combination is a key step of fusion technique, bands 
combination optimization must be followed by two principles: 
firstly, the physical significance of the selected bands are good 
and they are in different light area, that is to say the relevance 
of each bands are small; secondly, we should choose the bands 
with the largest information [3 lln this paper calculate the 
correlation coefficient matrix and OIF index, select the bands 
combinations in turn (table 1 and table 2). 
In the table of OIF index we can see that, the OIF index of the 
combination of bands ETM+3,4,5 is the biggest, and the 
correlation coefficient of bands3,4,5 is the smallest, so choose 
bands 3,4,5 as a fusion experiment. 
2.2 Image Registration 
The essence of image registration is according to the geometric 
correction of the remote sensing images; adopt a geometric 
transform to make the image unified to a same coordinate 
*Corresponding author: Yao Jing, School Of Geomatics,Liaoning Technical University , 123000 , 359 mailbox,Zhonghua 
street,Fuxin,China, yaojingl 124@163.com, telephone number:13941830365