IMAGE FUSION BASED ON EXTENSIONS OF INDEPENDENT COMPONENT 
ANALYSIS 
Mi Chen d ' *, Yingchun Fu b , Deren Li c , Qianqing Qin c 
a College of Education Technology, Capital Normal University, Beijing 100037,China - (mierc@hotmail.com) 
b College of Geography Science, South China Normal University, Guangzhou, 510631,China - (fyc226@163.com) 
c State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 
University, 129 Luoyu Road, Wuhan, China, 430079 
- (drli@whu.edu.cn,qqqin@lmars.whu.edu) 
Commission VII, WG VII/6 
KEY WORDS: image processing, integration, image understanding, fusion, land cover 
ABSTRACT: 
Remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes an image fusion 
algorithm based on extensions of independent component analysis (ICA) and multi-classifier system. Firstly a novel method of 
fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer 
set of directions and shapes than wavelet. As ICA not only can effectively remove the correlation of multi-spectral images, but also 
can realize sparse coding of images and capture the essential edge structures and textures of images, then using features extracted 
from the extension of ICA domain coefficients of the fused image, different classifiers corresponding to different image features are 
chosen in parallel style and the support vector machines are trained to classify the whole fused image as stack part in the proposed 
multi-classifier system. Experimental results show that the proposed algorithm can effectively improve the accuracy of image 
classification. 
1. INTRODUCTION 
Image fusion has received significant attention in remote 
sensing. It can be defined as the process of combining two or 
more source images from the same scene into a composite 
image with extend information content by using a certain 
algorithms. The fused image may provide increased 
interpretation capabilities and more reliable results since data 
with different characteristics. The process of image information 
fusion can be performed at signal, feature, and symbol levels 
depending on the representation format at which image 
information is processed. (Ranchin, 2003). 
The objective of image fusion is to improve the accuracy of the 
objective recognition and classification, which can support the 
decision making. The existing research results show that by 
fusing the panchromatic and multi-spectral images to gain the 
high spatial resolution multi-spectral fusion remote sensing 
images can effectively improve the accuracy of image 
classification. Besides fusing different classification results 
from different single feature sources at decision level can also 
be an effective way to improve the classification results. 
This paper proposes an image fusion algorithm of remote 
sensing images based on extensions of independent component 
analysis (ICA) and multi-classifier system. Firstly a novel 
method of fusing panchromatic and multi-spectral remote 
sensing images is developed by contourlet transform. Then 
using different features extracted from the extension of ICA 
domain coefficients of the fused images, a parallel and stack 
multi-feature and multi-classifier decision level image fusion 
algorithm is presented. The remainder of the paper is organized 
as follows. Section 2 recalls the concept of contourlet transform. 
Section 3 introduces the foundations and extensions of ICA. 
Section 4 highlights the algorithm of the decision fusion 
algorithm based on extension of ICA and multi-feature and 
multi-classifier system. Experiments results and comparisons 
are presented and discussed in Section 5. Conclusions are 
drawn in Section 6. 
2. CONTOURLET TRANSFORM 
The contourlet transform (M.N.Do, 2002) is an extension of the 
wavelet transformation in two dimensions using multi-scale and 
directional filter banks. The contourlet expansion of images 
consists of basis images oriented at various directions in 
multiple scales, with flexible aspect ratios. Thus the contourlet 
transform not only retains the multi-scale and time-frequency 
localization properties of wavelets, but also it offers a high 
degree of directionality and anisotropy. The contoulet transform 
is implemented in two stages: the subband decomposition stage 
and the directional decomposition stage. 
Recently developed contourlet transform can offer a much 
richer set of directions and shapes, and thus it is more effective 
than wavelet in capturing smooth contours and geometric 
structures in images. This paper proposes a novel method of 
fusing panchromatic and multi-spectral remote sensing images 
based on contourlet transform. 
Corresponding author.