The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1117 
Figure.4 Original SPOT(a), TM5/4/3 images(b) and fused images by contourle transform(c) 
Waterbody 
Naked land 
Dry land 
Figure.5 The three independent component bands(a,b,c) of the fused image by ICA(d) 
Figure. 6 classification results using different methods((a)Min Distance(b)Max Likelihood(c)Majority Voting(d)Bayesian 
Combination(e)Proposed Algorighm) 
Classification algorithm 
Total accuracy ( % ) 
Min Distance 
62.58 
Max Likelihood 
65.44 
Majority Voting Principle 
78.53 
Bayesian Combination Strategy 
81.80 
Proposed Algorithm 
82.21 
Table 1 the comparison of total accuracy of classification using 
different methods 
6. CONCLUSION 
Remote sensing image classification is an important means for 
quantified remote sensing image analysis, and remote sensing 
image fusion can effectively improve the accuracy of image 
classification. This paper proposes an image fusion algorithm 
based on extension of ICA and multi-classifier system. A novel 
method of fusing panchromatic and multi-spectral remote 
sensing images is developed by contourlet transform that 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 extensions of 
ICA domain coefficients of the images, different classifiers 
corresponding to different features are chosen in parallel multi- 
classifier style and the SVMs as stack fusion style are trained 
to classify the whole images in the proposed multi-feature and 
multi-classifier system. Experimental results show that the 
proposed algorithm can effectively improve the accuracy of 
image classification. 
REFERENCE 
Thierry Ranchin, Bruno Aiazzi, Luciano Alparone, Stefano 
Baronti, Lucien Wald,2003. Image fusion—the ARSIS concept 
and some successful implementation schemes, ISPRS Journal of 
Photogrammetry & Remote Sensing, 58 :4~18 
M.N.Do,M.Vetterli,2002 .Contourlets: A Directional 
Multiresolution Image Representation, IEEE ICIP:357~360 
Aapo Hyvarinen,1999. Survey on independent component 
analysis, Neural Computing Surveys, 2:94-128