1159 
THE STUDY ON IMAGE FUSION FOR HIGH SPATIAL RESOLUTION REMOTE 
SENSING IMAGES 
S.S.Han 3 ’ b ’ *, H.T.Li 3 , H.Y. Gu a ’ b 
1 Institute of Photogrammetry and Remote Sensing,Chinese Academy of Surveying and Mapping, Beijing 
100039,China - joinshanshan@163.com,lhtao@casm.ac.cn,haiyanl982709@126.com 
b Institute of Surveying and Geography Science, Liaoning Technical University, Fuxin 123000,China 
- joinshanshan@ 163 .com,haiyan 1982709@ 126.com, 
Commission VII, WG VII/6 
KEY WORDS: Remote Sensing, Image Fusion, Image Evaluation, Multiplication, Modified Brovey, High-Pass Filter, Smoothing 
Filter-based Intensity Modulation 
ABSTRACT: 
Remote sensing images fusion can not only improve the spatial resolution for the original multispectral image, but also preserve the 
spectral information to a certain degree. In order to find out the fusion algorithm which is suited for QuickBird images fusion, four 
simple fusion algorithms, Multiplication (MLT), Modified Brovey (MB), High-Pass Filter (HPF) and the Smoothing Filter-based 
Intensity Modulation (SFIM) algorithm have been employed for result evaluation. The study is based on a QuickBird sub-scene 
covering different land use. Numerical statistical methods such as Bias of Mean, Correlation Coefficient, Entropy, Standard 
Deviation and Average Grads are used to quantitatively assess the fused images produced using the above algorithms. The analysis 
indicates that the SFIM-fused image has the best definition as well as spectral fidelity, and is the best in high textural information 
absorption. Therefore it is suited for QuickBird image fusion best. 
1. INTRODUCTION 
Nowadays, remote sensing is developing to high-spectral 
resolution, high-spatial resolution, and high-time resolution. But 
as far as one and the same data is concerned, it is difficult to 
obtain the image of high-spatial resolution and high-spectral 
resolution at the same time. The information content of a single 
image is limited by the spatial and spectral resolution of the 
imaging system. Since the advent of the high spatial resolution 
satellite images, the merging of multiresolution images has been 
an important field of research. The fusion of remote sensing 
images can integrate the spectral information of single sensor or 
the information from different kinds of sensors (Couloigner,1998). 
In order to improve the dependability for extracting remote 
sensing information, and enhance the efficiency of using data. 
Literature has shown a large collection of fusion methods 
developed over the last two decades, such as the Multiplication 
(MLT) algorithm, Modified Brovey (MB) algorithm, High-Pass 
Filter (HPF) algorithm, the Smoothing Filter-based Intensity 
Modulation (SFIM) algorithm (Liu,2000a), the Principal 
Components Analysis (PCA), the Intensity-Hue-Saturation (IHS) 
and so on. All of the above-mentioned methods can realize the 
fusion of multi-spectral and high-resolution images, besides it can 
improve the spatial resolution and preserve the spectral 
information to a certain degree. For the moment, every method 
has its own advantages and disadvantages. Even if we use the 
same fusion to deal with the different images, we will get the 
different effect. In this paper, we evaluate the effect and 
applicability of different methods for high-resolution images 
through comparing the staple fusion methods. It can offer the 
reference to the fusion of high-resolution images. 
2. FUSION ALGORITHMS 
Data fusion provides several advantages (Ranchin,2003): 
preservation of computer storage space; enhancement of 
aesthetic and cosmetic qualities; improvement of spatial 
resolution; and analytical improvements. Each reason for data 
fusion relies on the following premise—for a data fusion 
model to be effective, the merged images should retain the 
high spatial resolution information from the panchromatic 
(Pan) data set while maintaining the basic spectral record of 
the original multi-spectral (MS) data (Carper et al, 
1990).Many methods have been developed in the last few 
years producing good quality merged images. This study 
analyzes four current data fusion techniques to assess their 
performance. The four data fusion models used include MLT, 
MB, HPF, SFIM algorithms. The reasons of selecting the 
above methods is mainly as follows: 1) They are all 
mathematically similar, for example, they are all statistical- 
based methods rather than color-related techniques; 2) They 
are simple and easy to be used; 3) They can be performed 
with any number of selected input bands, while some others 
like HIS only allow a limited number of input bands to be 
fused. 
2.1 MLT Algorithm 
The Multiplication model combines two data sets by 
multiplying each pixel in each band of the MS data by the 
corresponding pixel of the Pan data (Pohl.C,1997). To 
compensate for the increased Brightness Values (BV), the 
square root of the mixed data set is taken. The square root of 
* E-mail: joinshanshan@163.com; Phone: 86 10 88217730; Fax: 86 10 68211420; http://www.casm.ac.cn.