The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
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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.
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