The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
classification results are gotten, so decision level fusion of these 
classification results is needed. 
4.3.4 Decision Fusion Strategy: There are many strategies 
for combining classification results of each individual classifier, 
of which majority voting principle and Bayesian combination 
strategy are the most common used fusion method. 
1. Majority Voting Principle: The majority voting method 
selects the relevant class by polling all the classifiers to see 
which class is the most popular. Whichever class gets the 
highest vote is selected. This method is particularly successful 
when the classifiers involved output binary votes. 
2. Bayesian Combination Strategy: Bayesian combiners are used 
to carried out the classification according to the Bayes rule by 
selecting the class associated with the maximum average 
probability. 
3. The proposed Fusion Strategy: Different from the routine 
fusion strategy, this paper adopts SVMs C4 to fuse the different 
classification results corresponding to different image features 
to get the final fusion decision. Each classification results of 
respective classifiers serve as the input feature vectors for 
training SVMs, which can be regarded as stack multi-classifier 
fusion style and the continuation of the aforementioned parallel 
multi-classifier system. The total fusion topology is as figure 3. 
Moreover the above common fusion rules are also used to get 
the classification results corresponding to different features. 
Figure 3. paradigm of multi-feature and multi-classifier fusion 
5. EXPERIMENTAL RESULTS 
In this paper, to illustrate the proposed fusion procedure with an 
example, the data used for this experiment are SPOT 
panchromatic and Landsat TM 5/4/3 multi-spectral images, 
with the same size of 256x256 pixels. Figure. 4(a) ~ 4(b) are 
the panchromatic image and the corresponding multi-spectral 
images. The experimental area can be classified into water body 
(including river, paddy field), naked land (including road, 
residential area, bridge and other undeveloped filed) and dry 
land by human visual interpretation. The fused images using 
contourlet transform to fuse SPOT panchromatic and TM multi- 
spectral images are shown in Figure.4(c). 
The training area of water body, naked land and dry land are 
selected in the images, every category has two block of 16x16 
pixels training samples, theses samples of three component 
values in Ohta color space correspond to the first type of input 
feature vectors, i.e. Tl. 
The fused color images are transformed into the GMI. Then 
applying TICA to the GMI with 2x2 pixels of block in sliding 
window style to get four coefficients of TICA domain, 
meanwhile, the statistical parameters, such as mean, standard 
deviation, average gradient are computed for the second type of 
input feature vectors, i.e. T2. 
Turning the original multi-spectral images into 3x65536 pixels 
vectors and converting the original panchromatic image into 
1x65536 pixels vector to form 4x65536 pixels input vectors, 
applying ICA to the whole input vectors, three independent 
component bands are shown in Figure.5 (a)~ (c). The results 
indicate that the three independent components play good role 
in separate the water body, naked land and dry land. Meanwhile 
the resulting fused false color images in Figure.5(d) have higher 
spatial resolution compare to the original multi-spectral images. 
All these three independent components are chosen as feature 
vectors for classification, i.e. T3. 
The training areas for different classes are chosen in the images, 
following the above methods to extract spectral and ICA/TICA 
image features and training all the chosen classifiers, the trained 
classifiers are then applying to classify the whole fused images 
every pixel. Selecting suitable SVMs kernel function and 
parameter to train multi-category SVMs with the input feature 
vectors of every category obtaining from the afore parallel 
classifiers. The trained multi-category SVMs are applying to 
classify the whole fused images to gain the classification results. 
This paper chooses the radial basis kernel function: 
A"(a:,jc') =exp( ||jc - jc| /2a 2 )» where a =2, C=100o 
The classification results corresponding to different fusion rules 
are shown as follows. To test the effect of the proposed 
algorithm, the common fusion rules and the proposed novel 
multi-feature and multi-classifier algorithm for classification 
are showed in Figure.6 (c) and (e). Besides, the traditional min- 
distance method and the max-likelihood method of remote 
sensing image classification results are showed in Figure. 6(a) 
and (b).To evaluate the classification results objectively, the 
total classification accuracy is employed to describe the 
classification precision of the images by computing the degree 
of confusion between the statistical samples and the actual 
samples through sampling randomly in classification results. 
Table 1 is the comparison of total classification accuracy 
according to different classification methods.