International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
n-l n-l 
Mean i= SNR m 
LJ 
i=0 j=0 
n-l n-l 1/ 
St deviation = S: S| , (i — Mean _ iy [2 
E00 
n-l n-l 
Dissimilarity — SS SS P - j 
i=0 j=0 
where Pi; is the normalized grey level co-occurrence matrix 
(GLCM) with n x n size, where original n = 256 for 8bit data is 
pre-scaled to n=16 such that 
n-l 
SS pij) 
i, j-0 
2.2 Object-oriented analysis 
Problem with wide range of digital values representing one 
thematic class and overlapping values for individual classes 
were partly solved by the previous step — new channel 
calculations. This step has not solved all problems with 
overlapping values of individual classes. That was image 
segmentation what helped to divide image data into large 
regions representing important parts of land use. This 
segmentation allowed separating of urban parts from 
agricultural parts and from forest areas. 
The segmentation performed by object-oriented analysis 
defined in eCognition software simplified thematically 
complicated image data content. The object-oriented analysis 
comprises two parts. The first one prepares image data by 
creating segments from them and the second one allows their 
classification. 
The segmentation is based on heterogeneity evaluation. The 
heterogeneity is characterized either by spectral heterogeneity, 
or spatial heterogeneity and their mutual combination. Higher 
influence of spectral heterogeneity is accomplished by lower 
spatial heterogeneity while their sum is equal to |. The spatial 
heterogeneity compares either the compactness taking into 
account segment length and its number of pixels, or the 
smoothness expressing relation between segment length and the 
shortest segment length defined by a rectangle circumscribing 
the segment. 
2.3 Class definition 
Two-level class definition was the result of the presented 
methodology. The higher-level segmentation served to 
classification into basic regions: 
e old forest, 
e young forest 
e agricultural area, 
eurban area. 
The lower-level classification comprised higher number of 
-lasses. Each of them belonged into the only higher-level class. 
The following list shows classes for the lower-level (more 
456 
detailed classification). Class names are created from three 
parts. The first part part (F = forest, NF = nonforest, A= 
agricultural area and U urban area) defines the real situation of 
segments derived from the lower-level classification. The 
second part determines belonging to higher-level classification 
where OF means old forest, YF represents young forest, AA is 
an abbreviation for agricultural area and UA urban areas. The 
third part of class names indicates the lower-level class. The 
complete lower-level class names are: 
eF OF coniferous forest, 
eF OF. deciduous forest, 
eF OF forest older than 7 years, 
eNF OF forest up to 7 years, 
eF YF coniferous forest, 
eF YF deciduous forest, 
eF YF forest older than 7 years, 
eNF. YF forest up to 7 years 
eU YF road, 
eF AA tree, 
*A AA field, 
eU AA house 
eU AA road, 
eF UA tree, 
eNF UA green area, 
eU UA light house, 
eU UA dark house, 
eU UA house, 
eU UA road. 
The first part of names shows possible regrouping of certain 
classes, which were originally classified into thematically 
wrong higher-level classes. This process of regrouping brings 
new improvements into the analysis. The forest class is formed 
by three classes from old forest, three classes from young forest 
and one class from agricultural areas and one class from urban 
areas. Class definitions and their list were adapted to real 
situation in the image data and can change from to region to 
region. 
2.4 Segmentation 
The segmentation called multiresolution segmentation allows 
segmentation in more levels. The higher-level segmentation for 
image data division into thematically simple regions was 
calculated for high scale value (250) in the first image- 
processing phase. The original orthophoto and the channel 
calculated by median filter with 5x5 kernel size formed the 
input image data for the segmentation processes in the higher- 
level segmentation. 
The lower-level segmentation being the second image- 
processing phase was a repetition of the first one for lower scale 
value (35 — 50 according to image data). The segmentation 
process used the same channels. 
The influence of spectral heterogeneity varied from higher 
value for the higher segmentation level to lower value for more 
detailed classification where spatial heterogeneity played more 
important role. 
2.5 Classification 
After the segmentation, segments can be classified into classes. 
The segmented image data classification. was done by the 
   
     
   
    
   
   
  
  
   
   
  
  
  
  
  
   
    
     
  
  
  
  
   
   
   
   
   
   
    
    
    
    
  
   
   
  
  
   
    
   
   
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