A FUSION ALGORITHM OF HIGH SPATIAL AND SPECTRAL RESOLUTION IMAGES
BASED ON ICA
GuoKun Zhang* 3 LeiGuang Wang b Hongyan Zhang* c
a The Faculty of Tourism and Geographical Science Jilin Normal University, 1301 Haifeng Street,Siping, Jilin Province,
China, 136000
b State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,
129 Luoyu Road, Wuhan, China, 430079
c .College of Urban and Environmental, Northeast Normal University,5268 Renmin Street,Changchun, Jilin Province,
China, 13 0024
KEYWORDS: ICA Transform, image fusion, multiresolution analysis
ABSTRACT:
Independent component analysis (ICA) is a recently developed linear data analysis method. By using ICA method, the correlation
and redundancy of multispectral images can be eliminated. In detail, our algorithm can be divided into the following steps (as shown
in figure 1).Firstly, ICA transform is operated on MS imagery, and then, we get three new independent bands. Secondly, the discrete
wavelet transform with linear phrase is used to PAN image and independent components. Then, the rule for combining the ICA
coefficients with corresponding wavelet planes of panchromatic band is determined. Finally, inverse ICA is used to get the pan-
sharpened image. Compared to other algorithms of RS imagery fusion, our method reduces the data redundancy among MS image
bands and also preserves the spectral fidelity of the MS imagery as methods based on wavelet. Experiment result shows that our
method can avoid the artifacts in the fused images and fusion result is not sensitive to wavelet decomposition levels.
1. INTRODUCTION
Due to the physical constraint of the spatial information sensors,
there is a tradeoff between spatial and spectrum resolution of
remote sensing (RS)image (Aiazzi et al., ; Choi). The goal
fusing multispectral (MS) low-resolution remotely sensed
images with a more highly resolved panchromatic (PAN) image
is to obtain a high-resolution multispectral image which
combines the spectral characteristic of the low-resolution data
with the spatial resolution of the panchromatic image.(Choi,
2006).A fused product used for visual analysis may provide
better visual efficiency than the source image, the imagery can
also improve the classification accuracy.(Aiazzi et al., ; Choi, ;
Hill et al.)
Literature has a large collection of fusion methods, which can
be simply classified into several groups. One kind is methods
based on color space transformation, including HIS, Lab, YUV
and so on. One kind is base on the statistic methods, such as
PCA, Brovey transformation etc. Another group of fusion
algorithm is based on multi-resolution analysis (MRA) such as
pyramid decomposition and wavelet transformation (Aiazzi et
al.,; Wang et al.,2005).
Nowadays, the wavelet-based scheme for the fusion of
multispectral and panchromatic imagery has become quite
popular due to its ability to preserve the spectral fidelity of the
MS imagery while improving its spatial quality. But not all
kinds of wavelet transform are available for fusion problem.
Some shift variance of the transform can lead to artifacts in the
fused images. In order to avoid this problem, a novel fusion
algorithm combined Independent component analysis (ICA)
and wavelet filters with linear phrase was proposed in this paper.
The rest part is arranged as follows: in part two, the concept of
ICA and a first algorithm are introduced. In the third part, a
novel high frequency injection model in ICA domain is
proposed. Finally, the experiment result is analyzed in the part
4.
2. ICA AND FAST ALGORITHMS
Independent component analysis (ICA) is a statistical method
for transforming an observed multidimensional random vector
into components that are statistically as independent from each
other as possible, which is proposed by Jutten and Herault in
1991 (C and J, 1991; C and J,1999). The implication for feature
extraction in remote sensing has been found in many
works(Zhang et al.,2006).In the simplest way (Comon,1994),
the ICA model can be described as follows: there are n
unknown statistically independent components
S^,S 2 ,S 3 ,S 4 ,-’-S n , and theirs linear combinations with m
scalar variables V,, V 2 , V 3 • • • V m can be observed. That is:
v¡ =зд +a, 2 s 2 +o (3 i 3 +-4a A =^ а 1,Л <
7=1 )
i - 1,2,3••• m
1
'a:E-mail:ldxyzgk@163.com;phone(+86-0434)3291780
X: Corresponding author: E-mail:zhy@nenu.edu.cn;phone+8613074334258