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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 
the comparison of the two ‘high-pass’ images a histogram 
matching of the pan-sharpened image to the panchromatic 
orthoimage was indispensable. 
In Table 6 the correlation coefficients between the 
panchromatic band the pan-sharpened bands are presented. 
Given that the ideal value is 1, it can be noted that the pan- 
sharpened image preserves the characteristics of the initial high- 
resolution image it came from. 
  
Correlation coefficient between ‘high-pass’ 
panchromatic — ‘high-pass’ pan-sharpened image 
Pan-sharpened BANDS 
/ 2 3 4 
Panchromatic BAND 0.99 0.99 0.99 0.99 
  
  
  
Table 6. Criteria on the spatial evaluation of the pan-sharpened 
image 
5. CLASSIFICATION 
Having a pan-sharpened image of good spectral and spatial 
quality enables us to derive reliable thematic information 
through the process of classification. Classification of urban 
environment plays a key role in Urban Area Land-use Mapping, 
Urban Planning and Management, Establishment and Revision 
of GIS Database, Environment and Disaster Monitoring and 
Establishment of Telecommunication Network Station (Yu et 
al., 2002). With the development of high-resolution satellites it 
is now possible to collect and map thematic information from 
images of urban areas on large scales. However, high-resolution 
data does not mean high classification accuracy. In such cases, 
classification is more difficult due to the heterogeneity of urban 
Structures. 
5.1 The procedure of classification 
At first, a supervised classification with the method of 
maximum-likelihood classification was applied to the pan- 
sharpened image. Although a careful and detailed training was 
carried out, problems arose during the evaluation of the 
samples. Specifically, in the resulting classified image some 
streets were mixed up with buildings and territories of bare soil 
were misclassified as burnt forest. 
For this reason a different approach was decided. In urban areas 
roofs, streets and pavements are built of a similar material, so 
their similar reflectance creates problems during the 
classification process. The pan-sharpened image was separated 
to the urban part and the hilly tree-covered part and the 
classification was applied to these two image parts. 
Firstly, the set of classes was selected for each part and consists 
of streets, roofs, tiles, bare soil, low vegetation, trees and 
shadow for the urban part and trees, burnt forest, streets and 
rural streets for the hilly part. After the selection of the samples 
for each class, they were assessed through their histograms and 
the diagrams of their means and found to be representative and 
satisfactory. 
A fuzzy classification was introduced for both image parts and 
a distance file was created. The fuzzy approach was preferred 
because it allows more information on the partial class 
membership to be made available. The classified images were 
189 
chosen to have two layers of which Layer 1 contains class 
values for the best classification, Layer 2 for the second best. 
The pixels of the distance file consist of the values of the 
distance of the class means. With the help of the classified 
images and the distance files the Fuzzy Convolution operator 
creates a single classification layer by calculating the total 
weighted inverse distance of all the classes in a window of 
pixels, in this case a 3 X 3. Then it assigns the center pixel in the 
class with the largest total weighted inverse distance over both 
fuzzy classification layers. This reduces the phenomenon of 
'salt and pepper' in the classified image which the mixed pixels 
produce (Erdas, 1999). The final classified image is presented 
in Figure 7. 
  
Classes 
Low vegetation 
Streets 
Bare soil 
Trees 
Burnt forest 
Roofs ’ 
Tiles 
Shadows 
Rural streets 
MILITE D 
  
  
  
  
Figure 7. The classified image resulted from the application of 
the supervised classification 
5.2 Accuracy assessment of the classification 
The procedure of classification is completed when its accuracy 
is estimated. The error of the classification, which expresses the 
misclassification of some pixels, determines the degree of 
success of the procedure. For this reason a comparison is made 
between classified data and ground reference data and the error 
matrix, the accuracy report and the Kappa coefficient are 
calculated. The results are presented in Table 4. 
  
ACCURACY RESULTS FOR THE URBAN IMAGE PART 
ERROR MATRIX 
  
  
  
  
  
  
  
  
  
  
Classes |Low veg.|Streets|Bare soil| Trees | Shadows| Roofs | Tiles 
Low veg. 0 | | 0 I 0 0 
Streets 7 8 0 0 ] | 0 
Bare soil 0 0 8 0 0 | l 
Trees 0 0 0 10 0 0 0 
Shadows 0 0 0 0 10 0 0 
Roofs 0 2 0 0 2 6 0 
Tiles 0 0 | 0 1 0 8 
Toll test ÿ nl. 10 15 8 9 
pixels 
  
  
  
  
  
  
  
  
Overall Accuracy = 84.43% Kappa coefficient = 78.33% 
ACCURACY RESULTS FOR THE HILLY IMAGE PART 
ERROR MATRIX 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Classes Trees Burnt forest | Streets Rural streets 
Trees 2 0 0 
Burnt forest 0 8 0 0 
Streets 1 0 5 2 
Rural streets 0 | 0 
Total test pixels 7 11 5 9 
Overall Accuracy = 81.25% Kappa coefficient = 75.00%