1253 
AN IMPROVED IHS IMAGE FUSION METHOD 
WITH HIGH SPECTRAL FIDELITY 
Wen Dou a , Yunhao Chen b 
a Department of Geographic Information Engineering, Southeast University, Nanjing, Jiangsu, 210096 P. R. China 
- douw@seu.edu.cn 
b College of Resource Science, Beijing Normal University, Beijing, 100875 P. R. China - cyh@ires.cn 
Commission WG VII/6 
KEY WORDS: remote sensing, image fusion, histogram match, IHS 
ABSTRACT: 
Image fusion is a critical issue for remote sensing, and many algorithms have been developed. Image quality assessment of fused 
image might provide comparison between fusion methods, but the conclusion is not so general because different test images would 
lead to different assessment results. The paper studies on the relationships between image fusion methods aiming to reveal the nature 
of various methods. By doing so, we could compare the performance of spatial enhancement and spectral fidelity from mathematical 
form of image fusion processes. 
1. INTRODUCTION 
Methods based on the Intensity Hue Saturation (IHS) transform 
are probably the most popular approaches used for enhancing 
the spatial resolution of multispectral (MS) images with 
panchromatic (PAN) images (Tu, T.M., et al.,2004). The IHS 
method is capable of quickly merging the massive volumes of 
data by requiring only resampled MS data. Particularly for 
those users, not familiar with spatial filtering, IHS can 
profitably offer a satisfactory fused product. 
The main concept of the IHS method is based on the 
representation of low-resolution MS images in the IHS system 
and then substituting the Intensity component I with the PAN 
image. However, IHS and other so-called “component 
substitution” methods would introduce spectral distortion into 
the resulting MS images, appearing as a change in colors 
between compositions of resampled and fused multispectral 
bands. Such methods take redundant information of the PAN 
and MS imagery as the basis of image fusion, and hypothesize 
that PAN image and the Intensity component of the MS image, 
which is retrieved based on the RGB color model, contain 
almost the same information. That means PAN is taken as the 
high resolution intensity component of the high resolution 
multispectral data. Based on such hypothesis, spatial detail is 
the difference of PAN and the low resolution I component, and 
is injected into the MS image by substituting the I component 
with the PAN image. 
Unfortunately, it is impossible to construct I component 
containing same information as PAN image. That means spatial 
detail could be far from the truth when I component is 
constructed in an improper way. Therefore, introduction of 
spectral distortion is partly due to the construction of spatial 
detail. 
histogram matching should be implemented to make the PAN 
image has the same average and standard deviation with the low 
resolution I component as (1). 
= —-—I- fig (i) 
where fh is high resolution intensity component, and 
fJLj are average of PAN and I respectively, and tT/rjf and 
O’i are standard deviation of PAN and / respectively. 
It seems that the process is reasonable to make 1 and PAN 
comparable. However, an image vector space V is introduced 
to analyze histogram matching process, in which a single band 
image could be represented as a vector. For any vector £ and 
TJ in V, dot product is defined as 
<■ >= (2) 
where is covariation of ^and TJ ■ 
It is easy to prove that the vector space y with this operation is 
an inner product space. Module of vector and angle of two 
vectors o' is 
m= P) 
2. ANALYSIS ON HISTOGRAM MATCHING PROCESS 
For the IHS method, I component is constructed by the average 
of R,G,B band. Before / is substituted with PAN image,