. Istanbul 2004 
ted from three 
nonforest, A= 
real situation of 
sification. The 
el classification 
ig forest, AA is 
rban areas. The 
vel class. The 
uping of certain 
ito thematically 
grouping brings 
t class is formed 
om young forest 
:lass from urban 
adapted to real 
om to region to 
nentation allows 
segmentation for 
je regions was 
the first image- 
and the channel 
size formed the 
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second image- 
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ified into classes. 
as done by the 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
nearest neighbor classifier. The classification signature space 
used mean segment values of orthophotograph, channels 
calculated from median filter and Gauss filter with three 
different standard deviation values (equal to 2, 3, and 4). The 
last part of signature space was created by channels calculated 
as texture measures. Texture measures — mean, dissimilarity and 
standard deviation using three Haralick functions were 
calculated for three window sizes — 5x5 pixels, 11x11 pixels 
and 21x21 pixels. Classifications in both levels used the same 
signature space. 
3. RESULTS 
The higher-level classification had to be corrected in several 
cases (several segments). It is a relatively quick part of 
processing being performed during visual control and being 
done manually. Fig. 1 shows the original image data and Fig. 2 
shows result of the higher-level segmentation. The second-level 
classification result is on Fig. 3. 
The accuracy of classification result was controlled in random 
sample areas. The accuracy was calculated for producer’s 
accuracy PA(class 1) defined by 
PA(class zi ) + 
N 
k=1 
and for user's accuracy UA(class i) 
UA(class _i)= —+ 
2,4. 
k= 
The producer's accuracy estimates the probability that a pixel, 
which is of class i of the reference classification, is correctly 
classified. The total number of pixels of class. i in the reference 
classification is obtained as the sum of column 7. The user's 
accuracy estimates the probability that a pixel classified as 
class, i is actually of class. i. It compares the correctly classified 
number of pixels of class i with the total number of pixels 
classified as class i . The total number of pixels classified as 
class iis in the row i. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Classified Reference classes 
classes house | tree road field [forest up|deciduous| coniferous | Z of pixels 
to 7y 
house 8354 0 680 0 0 0 0 9034 
tree 358 | 1609 199 288 0 0 0 2454 
road 1831 0 4681 0 0 0 0 6512 
field 792 275 264 {297253 0 0 0 298584 
forestupto7y| 0 0 0 0 49813 0 0 49813 
deciduous 29 855 0 0 32792 | 24173 580 58429 
coniferous 0 415 0 0 9 964 81086 82474 
Z of pixels |11364| 3154 5824 | 297541 | 82614 | 25137 81666 507300 
Accuracies for individual classes 
Producer's PA| 0.74 | 0.51 0.80 1.00 0.60 0.96 0.99 
Users UA | 0.92 | 0.66 0.72 1.00 1.00 0.41 0.98 
  
  
  
  
  
Table 1. Results of classification accuracy. The best results are in yellow and the worst in gray 
The overall accuracy was 0.9 with kappa coefficient equal to 0.87.