1267 
A REGION-BASED TECHNIQUE FOR FUSION OF HIGH RESOLUTION IMAGES 
USING MEAN SHIFT SEGMENTATION 
Li Shuang 3 , Li Zhilin 3 ’ b 
a LIESMARS, Wuhan University, P.R. China - lishuangl29@gmail.com 
b Dept, of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 
- lszlli@polyu.edu.hk 
Commission VII, WG VII/6 
KEY WORDS: Image processing, Sharpening, Image understanding, Fusion, Geography 
ABSTRACT: 
This paper describes a region-based technique for fusion of high-resolution images. In this technique, mean shift segmentation is 
adopted to extract the features for high resolution image as a substitution of other segmentation methods (e.g. Canny operator) and 
Structure Similarity Index Metric (SSIM) is used to measure the region similarity. Experiments on IKONOS images are carried out 
to compare the results obtained from this new technique and those with Canny segmentation. It has been found that the results 
obtained this new technique is much better than the conventional ones in terms of spatial sharpness and spectral reservation. 
1. INTRODUCTION 
Image fusion is a process to combine two or more different 
images to form a new image by using certain algorithm (Phol 
and Genderen,1998). It takes place at three levels: pixel, feature 
and decision. Image fusion at pixel-level is the lowest 
processing level considering individual pixels or associated 
local neighbourhoods of pixels for fusion decision. In the past 
decades, large number of pixel level image fusion methods is 
proposed, e.g. Brovey, Intensity-Hue-Saturation, Principle 
Component Analysis, Wavelet transforms, etc (Zhen and 
He,2004). However, such methods often introduce colour 
distortion and/or block effect to high resolution image fusion. 
This is because a pixel is only a basic unit of information with 
no semantic significance. At the feature level, features from the 
input images will be first extracted (e.g. using segmentation 
procedures); and then fusion of these features will be operated 
by some rules. Comparing with pixel level image fusion, 
feature-level method is more meaningful. Because it can fully 
explore the characteristics of features to guide the image fusion 
process, such as region activity level, region similarity match 
measures and so on. 
More recently, a number of region-based feature-level image 
fusion techniques have been proposed (Zhang, 1997; Piella,2003; 
Lewis,2007). These techniques first transform the source 
images A and B to multi-scale representations by wavelet 
transforms; segmentation is carried out on the source image to 
get region representations of both images. Then by overlaying 
the two region representations, a shared region representation 
for these two images is obtained. And region activity level and 
similarity match measurements are calculated from each region 
to guide the fusion process. During the whole process, 
segmentation is the most important part because it directly 
influences the effect of fusion result. Previous work employs 
the watershed segmentation or Canny edge detection method 
and the results for fusion of high-resolution images are not very 
good. Therefore, this study aims to develop a new technique for 
fusion of high-resolution images. 
It has been found (e.g. Mo et al,2006) that the mean shift 
segmentation is more suitable for the segmentation of high 
resolution image and thus will be adopted in this study. 
Moreover, the Structure Similarity Index Metric (SSIM) 
proposed by Wang,(2002) for image quality assessment will be 
used (instead of region match measure which is commonly used) 
to guide the fusion process. 
Section 2 reviews the region based fusion. Mean shift 
segmentation for feature extraction is introduced in section 3. 
SSIM used for fusion decision making is described in section 4. 
Section 5 describes the evaluation of the proposed method and 
conclusions are made in section 6. 
2. REGION BASED FUSION: AN OVERVIEW AND A 
PROPOSAL 
The concept of region based fusion was first introduced by 
Zhang et al.,(1997) and developed by Piella et al.,(2003). 
Piella’s generic region based image fusion framework is shown 
in Figure 1.