225 
A NOVEL IMAGE FUSION METHOD BASED ON 2DPCA IN REMOTE SENSING 
Xue-ming Wu*, Wu-nian Yang 
RS and GIS Institute, Chengdu University of Technology, Chengdu 610059, China, 
nihaowxm@ 163.com, ywn@cdut.edu.cn 
KEYWORDS: Image Fusion, Remote Sensing, PCA, 2DPCA 
ABSTRACT: 
PCA-Based image fusion algorithm, which is widely applied in remote sensing, is a typical technique in image fusion. It is easy to 
implement and has good performance. However, PCA has two weaknesses. One is that an image must be transformed into a 1-D 
vector when PCA is applied to the image and can not utilize its structural information. The other is that the spectral information is 
badly lost in the fused image, although the spatial resolution is apparently improved. To avoid these disadvantages of PCA, this paper 
shows a novel image fusion method based on two-dimensional PCA (2DPCA), which is directly applied to the image matrices 
instead of 1D vector. Therefore, the structural information of the image is effectively utilized. Furthermore, with this new technique, 
not only the spatial resolution of the fused image is greatly improved, but also the spectral information is well preserved. The results 
of our experiment show the better flexibility of this method proposed in contrast to the traditional method. 
1. INTRODUTION 
The objective of image fusion is to use many images of the 
same scene provided by different sensors in order to provide a 
complete understanding of the scene, not only in terms of 
position and geometry, but more importantly, in terms of 
semantic interpretation (Henri, Maiter, et al., 1997; Towinn, 
Taxt, 1998). Image fusion technique has been widely applied to 
image processing, remote sensing, computer vision, and 
military (QIN Zheng, et al., 2007). PCA is one of the statistical 
methods for dimensionality reduction and correlation 
elimination. It has been widely used in remotely sensed image 
processing, including image coding, data compression, image 
enhancement, multitemporal dimensionality and image fusion, 
and so on (C. Pohl, J. L. Van Genderen, 1998). PCA-based 
algorithm on remotely sensed image is a typical technique in 
image fusion. PCA was first applied to image fusion in remote 
sensing by Chevas P S, et al. (Chevas P S, et al., 1984; Chevas P 
S, 1986; Chevas P S, et al., 1991) since 1980s. They integrated 
multisensor and multiresolution images of the same scene, for 
example, merging the Landsat TM images and SPOT 
panchromatic images, to get new images about the scene. Since 
then, further research on the technique was done by Cliche, G. 
Bonn (Cliche, G Bonn, 1986), Ashbindu Singh (Ashbindu 
Singh, 1985), James R. Carr (James R. Carr, 1998), and C. Pohl, 
J. L. Van Genderen et al. (C. Pohl, J. L. Van Genderen, 1998). 
This makes the PCA-based algorithm became an important 
image fusion method in remote sensing. A typical application 
of PCA-based algorithm is to integration the multispetral 
images with low spatial resolution and the panchromatic 
images with high spatial resolution. It is easy to implement and 
has good performance. 
However, PCA has two weaknesses. One is that an image must 
be transformed into a 1-D vector when PCA is applied to the 
image and can not utilize its structural information. The other is 
that the spectral information is badly lost in the fused image, 
although the spatial resolution is apparently improved. 
2DPCA, which is first proposed in 2004 by Jian Yang, et al. 
(Jian Yang, et al., 2004), is a technique on image analysis. 
2DPCA is directly applied to the image matrices instead of ID 
vector, and then the eigenvalues and the eigenvectors are 
evaluated. In contrast to PCA, 2DPCA has better performance 
in feature extraction and face recognition; especially it takes 
less time and improves recognition rate (Jian Yang, et al., 2004). 
Therefore, more attention has been paid on it (D.S. Guru, P. 
Nagabhushan and B.H. Shekar, 2007; Hui KONG, Xu-chun LI, 
et al., 2005; Hui KONG, Lei WANG, et al., 2005). 
Now that 2DPCA has upstanding performance in feature 
extraction and face recognition, can it be introduced in image 
fusion? If it can, the performance of 2DPCA is better or worse 
than PCA? This is the objective of this paper. After studying the 
characteristic of PCA-based algorithm and 2DPCA, we 
proposed a novel image fusion algorithm based on 2DPCA, 
which is an entirely different algorithm in contrast to 
PCA-based method. In the novel technique, not only 2DPCA is 
introduced into remotely sensed image fusion, but also it has 
better performance than PCA-based method. It is of importance 
that the characteristic of 2DPCA-based technique overcome the 
weakness of that of PCA-based. With this new technique, not 
only the spatial resolution of the fused image is greatly 
improved, but also the spectral information is well preserved. 
The remainder of this paper is organized as follows. In Section 
2, PCA-based image fusion algorithm on remotely sensed 
images is described. The idea of 2DPCA and the proposed 
2DPCA-based method are shown in Section 3. In Section 4, the 
experimental results are presented for PCA-based and 
2DPCA-based algorithms to demonstrate the effectiveness of 
2DPCA-based method. Finally, conclusions are summarized in 
Section 5. 
2. PCA-BASED ALGORITHM 
PCA-based image fusion algorithm on remotely sensed images 
can be summarized to three steps as follows. 
* Corresponding author.