'stanbul 2004 
ca 
  
   
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
  
  
€ Forest 
YF older than 7 years 
AA tree 
EE on ® OF coniferous up to 45 years 
Se) -@ OF coniferous older than 45 y 
a qu OF deciduous 
N p. UA tree 
€ ) Non-forest 
: UA green areas 
E AA fields 
YF forest up to 7 years 
OF forest up to 7 years 
10 Urban area 
| Ea UA road 
i UA dark house 
rt UA light house 
UA house 
AA house 
AA road 
YF road 
  
Figure 3. Lower-level classification 
Results shows that certain parts of roads were classified into 
class houses. Pixels from class houses were classified into trees, 
roads, and fields. Classified class called tree comprised pixels 
from houses, roads, and fields, class roads was a class having 
also pixels from houses. Forest classes showed very good 
results for the coniferous forest. The forest younger than 7 years 
old was classified partly into the deciduous forest. The best 
results were obtained for fields. Results reliability is higher 
between urban areas and forests or urban areas and fields. 
Smaller reliability was found between urban classes — houses 
and roads, etc. 
4. CONCLUSION 
The B&W photograph classification can be performed with a 
relatively high accuracy. There are three necessary conditions 
offering such good results. New channels have to be calculated 
from original data (photograph) — channels calculated by 
filtering where median and Gauss filters are used, and channels 
using Haralick functions. Careful testing of kernel sizes for 
filtering and careful choice of filter window sizes for Haralick 
functions compared to final classified object sizes can 
sufficiently improve classification accuracy. The presented 
project used kernel sizes smaller than the smallest classified 
objects. Filter window sizes were smaller, equal and a little bit 
larger than these objects. The higher number of Haralick 
functions improves classification accuracy. 
Object-oriented analysis using image segmentation followed by 
segment classification is the second necessary condition for 
good results of classifications. 
Two level segmentation in the reverse order of segmentation 
(from higher to lower) is the third condition. The higher-level 
segmentation ensures fragmentation of image data with 
overlapping pixel values for different classes into thematically 
closer and smaller image parts whose further segmentation and 
classification offers good results. The lower level classification 
is performed individually for already fragmented parts of image 
data. 
'The confusion in resulting classification can be found in urban 
areas. The overall accuracy was about 90 per cent. However, the 
accuracy for individual classes varied from 50 per cent to 100 
per cent. Low accuracy values were in case of two classes — 
trees whose pixels were classified into deciduous and 
coniferous forests. The class tree did not distinguish tree types. 
It was a class describing individual trees or tree groups out of 
forest areas. Rather wrong distinguishing can be found between 
very young forest and deciduous forest. It is difficult to 
distinguish these two classes even during visual interpretation 
especially for forest age younger than 40 years. 
REFERENCES 
Baatz, M. and Schápe, A., 1999. Object —oriented and multi- 
scale image analysis in semantic networks. Proc. Of the a 
International symposium on operationalization of remote 
sensing, August 16-20, , Enschede ITC. 
Borisov, A.N., Kashin, V.B., Khlebopros, R.G., 1989. Method 
for indication of horizontal structure of tree stands. Doklady — 
Biological Sciences, 1989. 293: (1 — 6), 132 - 133. 
Halounová, L., 2003. Textural classification of B&W aerial 
photos for the forest classification. Proc. of the 23". symp. of 
EARSeL, Gent, Belgium, June 2-5, 2003: 173-179.