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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
4. IMAGE FUSION ALGORITHM BASED ON 
EXTENSIONS OF ICA AND MULTI-CLASSIFIER 
SYSTEM 
This part introduces the decision level remote sensing image 
fusion algorithm. Existing studies have show that by fusing 
multi-spectral images and panchromatic image to get the high 
quality fusion images can improve the accuracy of classification 
than just using single source image. This paper uses the 
panchromatic image, multi-spectral images and the resulting 
fused images for researching objects, extracting the spectral 
features, texture features and the features in ICA and TICA 
transformation domain, using different classifiers to get the 
classification results corresponding to different features and 
applying the method of multi-classifier system to obtain the 
final classification of land cover from remote sensing images. 
4.1 Image Fusion Based on Contourlet Transform 
Coregister both images and resample the multi-spectral images 
to make its pixel size equal to that of the panchromatic image in 
order to get perfectly superposable images. Here only R/G/B 
three channels are considered. The images are firstly 
decomposed by contourlet transform, getting low frequency and 
high frequency coefficients in different resolutions and different 
directions. Then combining atrous wavelet, the fusion 
procedure is choosing different rules on particular sets of 
contourlet coefficients that correspond to high and low 
frequency bands. The high-frequency coefficients of the 
panchromatic image substitute all the high-frequency 
coefficients in R/G/B three channels. The panchromatic image 
is decomposed by atrous wavelet, getting a group of wavelet 
plane coefficients, then adding these wavelet coefficients to the 
low-frequency contourlet coefficients, that is further extracts 
the detail information of panchromatic image for fusing 
application. Final fused images are obtained by using reversed 
contourlet transform. 
The proposed method can get more information in the fused 
results and the spectral reserving character is quite well. The 
contourlet transform image fusion method offers a desirable 
result to improve spatial resolution and information of the fused 
images, which get ready for the next classification procedure. 
4.2 Image Feature Extraction 
4.2.1 Spectral Feature: The spectral features of multi- 
spectral images are the most essential features, here the three 
channels of high spatial resolution fused images are considered. 
Besides the spectral features extracted in the original R/G/B 
color space, other image features can be gotten by transforming 
the multi-spectral fused image into different color space. How 
to choose the suitable color space is an important factor for 
different color space can define different useful spectral 
features. So there are two key points should be considered in 
choosing color space, one is that the color space can present the 
irrelevant color features, the other is that the color space 
remains constant in different illumination conditions. Based on 
the above considerations, the paper chooses the Ohta color 
space(Ohta,1985) which can be expressed as following. 
I, =(R + G + B)/3 
< I 2 = (R - B)/2 (14) 
I 3 =(2G-R-B)/4 
The components in Ohta color space are irrelevant, so it can 
well apperceive the change of color in statistical view. Ohta 
color space is gotten by linear transform of RGB color space, 
here I] is the intensity component, I 2 and I 3 are the almost 
orthogonal color components. The feature vectors obtained 
from Ohta color space are denoted by T t here. 
4.2.2 Texture Feature of TICA basis: Since the original 
panchromatic image has high spatial resolution and low spectral 
resolution, different objects have the same gray value or the 
same object has different gray values in the panchromatic 
image sometimes. As the fused images can have high spatial 
and spectral resolution simultaneously, the fused color images 
are transformed into the grayscale image named grayscale 
modulation image (GMI) here by specific algorithm. GMI is 
useful information source because its spatial resolution is 
similar to the original panchromatic image and its gray spectral 
values can reflect different objects much better. So by applying 
TICA to the GMI block by block in sliding window style, the 
texture features of TICA basis can be extracted. 
In order to uncover the underlying structure of an image, it is 
common practice in image analysis to express an image as the 
synthesis of several other basis images. These bases are chosen 
according to serve some specific analysis tasks. The advantage 
of the TICA basis is that the estimated transform can be tailored 
to the needs of the application. A set of images with similar 
content to the GMI is selected for training the desired bases. 
Then using the TICA basis the GMI is transformed into the 
TICA domain in sliding N X N window style. All of these 
extracted TICA features can well reflect the structure and 
texture information of the fused images. The TICA feature 
vectors are denoted by T 2 here. 
4.2.3 Independent Component Feature: The existing 
studies show that the correlation between the bands of multi- 
spectral images sometimes brings ill effect in image 
classification. ICA not only can remove the correlation in the 
bands of multi-spectral images, but also can makes the resulting 
components mutual independent as much as possible. The every 
resulting band of independent component embodies a 
concentrated reflection of certain ground objects, increasing the 
degree of separation between different ground objects. 
Therefore independent component analysis can effectively 
remove the unfavorable influence and raise the accuracy of 
classification. 
Multi-spectral images can be regarded as the linear combination 
of multi-source mixture signals in some sense due to their low 
spatial resolution. This paper treats the panchromatic and multi- 
spectral images as four dimensional mixture signals and adopts 
ICA to obtain another fusion scheme of multi-spectral and 
panchromatic images to get three dimensional multi-spectral 
images. Though the resulting independent components can not 
reserve the original spectral characteristics very well, they can 
express the sharpened multi-spectral images and resolve the 
problem of unmixing the mixed multi-spectral images pixels in 
the hypothesis of linear spectral mixture model, which lay the 
foundation for the next step of properly classification. The