The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
2. IMAGE FUSION TECHNIQUES 
2.1 Smoothing Filter-based Intensity Modulation 
SFIM is spatial domain fusion method based on smoothing low- 
pass filters. It is defined as: 
Jl *b h 
B SFIM - B M 
(1) 
Where B L is LSR image, B H is HSR image, B M is a simulation 
image of LSR image, and it derived from B H image using an 
average low pass filter. 
SFIM can be understood as a LSR image modulated directly by 
high spatial frequency information and the fused image is 
independent of the contrast and spectral properties of the HSR 
image. It is critical for the selection of a low pass filter kernel to 
generate B M image. The minimal filter kernel size is decided 
based on the resolution ration between the higher and lower 
resolution images (Liu,2000). For example, the minimal filter 
kernel size should adopt 4x4 filter window for fusing LSR 
image with HSR image of Quickbird or IKONOS. SFIM-fused 
images have fine spectral information preservation with the 
LRS images, but the edges between different features are 
blurred in SFIM-fused productions (Liu,2000). 
2.2 Wavelet Transform 
More recently, Discrete WT (DWT) has started playing a role 
in image fusion. In general, DWT consists of wavelet 
decomposition (Fig. 1) and reconstruction (Fig. 2). There are 
following steps in image fusion based on DWT. 
1) Selecting biorthogonal wavelet bases used in wavelet 
decomposition and reconstruction. 2 
W r , W r " , W; and W r D (Fig. 1) are approximation 
coefficients, horizontal coefficients, vertical coefficients and 
diagonal coefficients, respectively. 
3) The low frequency sub-image of LSR image and the high 
frequency sub-images of HSR image are selected to generate 
the fused image by wavelet reconstruction (Fig. 2). 
Low- pass 
К 
к 
к 
-► 
к 
Low- pass 
f+ x » sub- image ► 
Ж 
h 
High- pass 
Low- pass 
V 
+/‘ 
A 
Fused 
image 
w: 
<p 
► 
K 
W D 
<p 
K 
S sub- image ► 
If 
JL 
High- pass 
High- pass 
Figure 2. The wavelet reconstruction of two-dimensional DWT 
The key issues in DWT are selections wavelet bases, 
decomposition levels and replacement sub-images. Different 
selections form different fusion models. Depending on the 
optimization control, fusion methods based on DWT can better 
preserve image spectral properties than IHS transform and BT. 
However, because of more complicated and time-consuming 
processing and critical requirement for the co-registration 
accuracy, these techniques are less popular than IHS transform 
and BT for remote sensing applications which prefer fast 
interactive processing and real time visualization (Liu,2000). 
2) The selected wavelet bases are applied to decompose LSR 
and HSR images. After decomposition at any level, the low 
frequency sub-image (commonly termed “approximation” 
coefficients) is passed to the next decomposition. High 
frequency sub-images (termed “horizontal”, “vertical”, and 
“diagonal”) are retained for reconstruction. 
2.3 Brovey Transform 
One of widely used image fusion methods is BT based on 
chromaticity transform and RGB space transform. It is a simple 
and efficient technique for fusing remotely sensed images. BT 
is defined as: 
image 
Low- pass 
Low- pass 
h 
sub-image 
Г к 
w, 
H 
High- pass 
Low-pass 
К 
wf 
► hy > sub-image 
High- pass 
High- pass 
Figure 1. The wavelet decomposition of two-dimensional DWT 
R BT ~ j X Bh » G B t ~ j x B H 
n r R + G + B 
D BT ~ J * D H'> 1 ^ 
(2) 
Where R, G and В are three bands LSR images. 
Only three bands LSR images are involved in standard BT. 
Thus, there are different choices when the total number of LSR 
images is more than three. For example, there are four different 
choices in the case of fusing four bands LSR images of 
Quickbird or IKONOS. To solve the problem, Modification BT 
(МВТ) fusion method based on the principle of BT is 
introduced in this paper. МВТ is defined as: 
1228