The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1145 
4.2.3 Supervised Classification Accuracy Evaluation: Do 
supervised classification with the original image and the SFIM 
fusion image choosing 5,4,3bands after the bands selection, and 
evaluate the accuracy of the classification, the classification 
accuracy of the results are showed in table 5 
type 
XS image 
SFIM fusion image 
Total 
82.81% 
90.23% 
accuracy 
Kappa index 
0.7668 
0.8673 
Table 5.Comparative data of image supervised 
classification accuracy 
By comparing the total accuracy and Kappa index of the two, 
we can see that: the accuracy and Kappa index of the 
supervised classification of SFIM fusion image are much 
higher than the XS images. 
Through the accuracy analysis of the unsupervised 
classification and supervised classification, can generally know 
the character of the above five algorithm, on the basis of ETM+ 
classification, select SFIM fusion image as the basic image; 
Brovery fusion image as the small information leading to the 
lower classification accuracy; HPF fusion image owing to the 
better spectral fidelity and more high-frequency information 
can be used as the auxiliary image of the visual interpretation; 
PCA, ML fusion image has high integration of high frequency 
information, which has a certain value in the extraction of the 
city internal structure. 
5. CONCLUSIONS AND OUTLOOK 
This paper introduces the basic concepts and theory of image 
fusion, and discussed a variety of fusion method. Summarize 
the quantitative evaluation criteria of the mean, standard 
deviation, correlation coefficient, entropy, the average grads 
and so on, measure and compare each fusion algorithm and 
obtained many useful conclusion. 
But this study make fusion analysis with the panchromatic 
image and multispectral images in the same satellite system of 
Landsat-7 ETM+, as different types of sensors have different 
data types, it should be more complex in the process of fusion 
and the evaluation, yet has to do further studies with specific 
issues. 
REFERENCES 
[1] Zhao Yingshi,2003. Remote sensing application principles 
and methods.Bdpng-.Science press,pp..253-254. 
[2] Xu Hanqiu. Study on Data Fusion and Classification of 
Landsat 7 ETM + Imagery .Journal of Remote 
Sensing,2005,9(2), pp. 186-194. 3 
[3] Shi Yongjun,2003. Forest remote sensing classification 
technology research take the northwest mountain area of 
Zhejiang as an example.Zhengjiang: Zhejiang university, 36- 
50 
[4] Sollerg S.Jain A.K.Taxt.T.A Makov random field model for 
classi fication of multisource satellite imagery .IEEE Trans, on 
Geosciences and Remote Sensing, 1996,34( 1 ),pp. 100-113. 
[5] Wang Wenjie,2006. Image fusion technology based on 
wavelet transform.Chinese Academy of Sciences, a master's 
degree thesis. 
[6] Zheng Yongsheng, Wang Renli,1999. Dynamic monitoring of 
remote 5e«5wg-,Beijing:PLA publishing . 
[7] Jia Yonghong,2005. Multi-source remote sensing data fusion 
technology,Beijing: Mapping Press,. 
[8] Smara Y,Belhandj-Aissa A, and Sansal B.Multisources ERS- 
1 and Optical Data for Vegetal Cover Assessment and 
Monitoring in a semi-arid Region of Algeria [J],International 
Journal of Remote Sensing, 1998,19(18), pp. 3551-3568. 
[9] Musa M K A,Hussin Y A.Multi-data Fusion for sustainable 
Forest Management : A case study from Northern Part of 
Selangor ,Malaysia[J]./»/mzatz'o«a/ archives of photogrammetry 
and Remote 5e«sz'«g,2000,33(B7),pp.71-80. 
[10] Pan Xueqin,2005. The comparison and analysis of data 
fusion of remote sensing z'zwage.Kaifeng:Henan university. 
Appendix 
Supported by the Ministry of Education of Doctor Spot 
Foundation (20050147002); 
College of Liaoning Province Emphasis Laboratory Item 
(20060370)