Full text: Proceedings, XXth congress (Part 3)

stanbul 2004 
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International Archives of the Photogrammetry, Remote Sensin g and Spatial Information Sciences, Vol XXX V, Part B3. Istanbul 2004 
  
  
  
  
  
Confidence interval [Belief(A), Plausibility(A)] Identified 
Feature |m(0) ; : = 
Texture Woodland Grassland |Inhabitantarea| Water field result 
Fractional|0.482| 0.280 | 0.762 | 0.089 0.571 | 0.050 0.532 0.110 0.592 Unknown 
Woodland Entropy 0.162| 0.678 | 0.840 | 0.087 0.249 | 0.032 0.194 0.051 0.212 Woodland 
Fused |0.105| 0.727 | 0.832 | 0.081 0.187 | 0.030 0.136 0.060 0.165 Woodland 
  
Fractional|0.446| 0.132 | 0.578 | 0.225 
0.671 | 0.119 | 0.565 0.177 0.623 Unknown 
  
Grassland Entropy 0.233} 0.115 | 0.343% | 0355 
0.588 | 0.072 0.305 0.164 0.397 Unknown 
  
Fused [0.162 0.121 | 0.283 | 0.481 
0.643 | 0.066 | 0228 | 0.170 | 0.332 Grassland 
  
Inhabitant |Fractional|0.543| 0.137 | 0.680 | 0.126 
0.669 | 0.283 | 0.826 0.101 0.644 Unknown 
  
area Entropy 0.475! 0.062 | 0.527 | 0.089 
0.564 | 0.263 | 0.728 0.140 0.615 Unknown 
  
Fused |0.234| 0.080 | 0.314 | 0.137 
0.371 | 0.341 | 0.635 | 0.154 0.388 
Inhabitant 
area 
  
Water field|Fractional|0.426| 0.145 | 0.571 | 0.181 
0.607 | 0.119 | 0.545 0.238 0.664 Unknown 
  
Entropy 0.1621 0.113 | 0305 | 0183 
0.375 | 0.148 | 0.340 | 0.464 0.656 
Water field 
  
  
  
  
  
  
  
  
Fused |0.182| 0.095 | 0.277 | 0.171 
0.353 | 0.110 | 0.292 | 0.463 0.645 
  
  
  
  
  
  
Water field 
  
Tabel 2. Part of confidential intervals and uncertainty probability 
Table 2 is part of confidence intervals and uncertainty 
probabilities. The confidence interval obtained from the fused 
feature is smaller than that from the single feature 
correspondingly, Belief and Plausibility obtained from the fused 
feature are higher than that from the single feature 
correspondingly, the multi-feature fusion technique based on 
Dempster-Shafer's evidential reasoning for classification is apt 
to identify textures correctly. Comparison with uncertainty 
probability m(6), the feature fusion technique reduces m(0) and 
enhances the power of identify. 
4. CONCLUSION 
A new multi-feature fusion technique based on Dempster- 
Shafer's evidential reasoning for classification of image texture 
is presented. The proposed technique is divided into three main 
steps. An example is provided. The performance of the method 
is investigated with some aerial photos in some area. Compared 
with the results obtained from the single feature, the results 
obtained from the multi-feature fusion indicate the multi-feature 
fusion technique based on Dempster-Shafer's evidential 
reasoning for classification is stable and reliable, and efficiently 
improve the accuracy of classification. 
S.REFERENCES 
Galloway M., 1975, * Texture classification using gray level 
run length", Computer graphics and Image Processing, 4, 
ppl72-179. 
Haralick R. 1979, Statistical and structural approaches to 
texture. Proceeding. IEEE, 67(5), pp786-804. 
Huang Guilan, Zheng Zhaobao, 1998a,The application of 
fractal geometry in image texture classification. Journal of 
Survey& Mapping, 24(1), pp283-292. 
Huang Guilan, Zheng Zhaobao, 1998a, Texture models applied 
in image texture classification. Journal of WTUSM, Vol.23, 
No.1, pp40-43. 
Li Deren, Zhanglixian, 1993a,The statue and methods of image 
texture analisys. S&T of WTUSM, No.3, pp30-37. 
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Li Deren, ZhangJixian, 1993b,The statue and methods of image 
texture analisys. S&T of WTUSM, No.4, pp16-25. 
P.L. Rogler, 1987,Shafer-Dempster reasoning with application 
to multisensor target identification systems, IEEE Trans, on 
S.M.C, Vol.17, No.6, pp968-977. 
Yang Jingyu, Wu Yongge, Liu Leijian Etc, 1994, Data Fusion 
Technique in war field. Weapon Industry! Beijing , pp66-67. 
 
	        
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