. 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
3.2 Information Extraction from Radarsat-1 SAR Image 
Considering some of the fractals have the same fractal 
dimensions but have the different textures, on the basis of 
textural analysis using fractal dimensions, this research further 
introduces multi-fractal theory and the second-order statistic 
lacunarity as the effective supplements to fractal dimension for 
textural analysis. Samples of typical ground objects were 
extracted, then the multi-fractal q-D(q) curve and the lacunarity 
L-C(L) curve were plotted in order to quantitatively determine 
the optimum parameters for effective fractal features extraction 
(Figure 2,3). From Table 2 and Table 3, we can see that when q 
equals to 8 and -8, L equals to 2, there is good separability 
among ground objects, so they were selected as the effective 
parameters for feature extraction. 
q-D(Q) 
-waterbody 
-bare land 
village 
town 
-industrial premises 
-reed 
-cropland 
-vegetation plot 
-10 -9-8 -7 -6 -5 -4 -3 -2 -1 0 2 3 4 5 6 7 8 9 10 
q moment 
Figure 2. Multi-fractal q-D(q) curve of the typical ground 
objects 
Lacunarity 
1.5 
j—I—I 1—I—I—I—I 
•build-up land 
-waterbody 
bare land 
vegetation 
2 3/ 4 5 6 7 8 9 10 
Figure 3. Lacunarity L-C(L) curve based on DBC method 
Based on SVM, and using the seven multi-scale and multi 
texture textural features mentioned in 2.2 as input, Radarsat-1 
SAR image can be classified (Figure 4). 
Legend 
bui Id-up land 
water body 
bare land 
vegetation 
Figure 4. Classification result of SAR image based on multi 
scale and multi-texture feature fusion and SVM 
(part of the test site) 
In order to test the classification performance of different 
textural features, based on SVM, this research designed a 
classification scheme (Table 3). From Table 3 and Table 4, it 
can be seen that the fused seven features can reach better 
classification performance than any single or any other 
combination of the features and the improvement of the 
classification accuracy is significant. 
Group 
No. 
Femme for classification 
Overall 
accuracy % 
Kappa 
coefficient 
1 
multi-scale and multi-texture feature (Multi-scale 
GLCM+Three types of fractal features) 
69.8926 
0.4916 
2 
Multi-scale GLCM 
67.2711 
0.4680 
3 
Three types of fractal features (FD+multiFD+Lacu) 
66.8600 
0.4452 
4 
Multi-scale GLCM+FD 
68.1001 
0.4690 
Multi-scale GLCM+multiFD 
68.2594 
0.4700 
Multi-scale GLCM+Lacu 
68.5601 
0.4751 
5 
Multi-scale GLCM+FD+multiFD 
68.5857 
0.4789 
Multi-scale GLCM+FD+Lacu 
69.4600 
0.4803 
Multi-scale GLCM+multiFD+Lacu 
69.4831 
0.4889 
6 
SAR intensity image 
54 4739 
0.2395 
SAR hackscattering coefficient image 
45.9268 
0.2139 
Table 3. Classification accuracy analysis of SAR image based 
on multi-scale and multi-texture feature fusion and SVM 
Z value 
Group 1 
Group 2 
Group 3 
Group 1 
Group 2 
37.76787 
Group 3 
57.63155 
19.93228 
Table 4. Comparison of Z statistic 
The classification performance between traditional maximum 
likelihood classification and SVM was also compared in this 
research. Using the same seven fused textural features as input, 
the classification accuracy is compared and Z statistic is given 
in Table 5. From Table 5, we can see that overall accuracy of 
SVM classification is higher than that obtained using MLC by 
10%, Kappa coefficient is improved, and the Z statistic is 
71.6227. It can be concluded that SVM is an effective classifier 
for textural features, and it can significantly improve the 
classification accuracy compared with the MLC classifier. 
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