Cognition it was 
hod used in PCI 
benefit from the 
ith its high spatial 
onent of the four 
/as substituted by 
1annel. This new 
as re-transformed 
sformation. 
SSIFICATION 
ognition software 
nis means that the 
ly be represented 
' combined with 
2s can be used to 
generated image 
| within a class 
er-class and thus 
-classes or to its 
behaviour of the 
an be aggregated 
nantic hierarchy. 
umber of smaller 
roaches of image 
ch (see Benz, U., 
d performing the 
objects using the 
per-level image 
while the lower- 
ts. 
their physical 
js names and the 
respect to two 
e image objects 
ing inheritance 
tion task while 
ne. 
of the network's 
nships or being a 
an improvement 
hierarchy. 
semantic groups 
d “classification- 
iguous segments 
S. The semantic 
neighbourhood 
len working with 
1datory, the latter 
user's objectives 
1eters should be 
suiting with the 
B3. Istanbul 2004 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
   
  
e Scale parameter: this parameter indirectly influences the 
average object size. In fact this parameter determines the 
maximal allowed heterogeneity of the objects. The larger 
the scale parameter the larger the objects become. 
e  Color/Shape: with these parameters the influence of color 
vs. shape homogeneity on the object generation can be 
adjusted. The higher the shape criterion the less spectral 
homogeneity influences the object generation. 
e  Smoothness/Compactness: when the shape criterion is 
larger than 0 the user can determine whether the objects 
shall become more compact (fringed) or more smooth. 
Segmentation phase is followed by the classification of images. 
eCoginition software offers two basic classifiers: a nearest 
neighbour classifier and fuzzy membership functions. Both act 
as class descriptors. While the nearest neighbour classifier 
describes the classes to detect by sample objects for each class 
which the user has to determine, fuzzy membership functions 
describe intervals of feature characteristics wherein the objects 
do belong to a certain class or not by a certain degree. 
  
  
  
  
  
Figure 2. Hierarchical network of image 
Thereby each feature offered by eCognition can be used either 
to describe fuzzy membership functions or to determine the 
feature space for the nearest neighbour classifier. A class then is 
described by combining one or more class descriptors by means 
of fuzzy-logic operators or by means of inheritance or a 
combination of both (see Fig. 2). As the class hierarchy should 
reflect the image content with respect to scale the creation of 
level classes is very useful. These classes represent the 
generated levels derived from the image segmentation and are 
simply described by formulating their belonging to a certain 
level. Classes which only occur within these levels inherit this 
property from the level classes. This technique usually helps to 
clearly structure the class hierarchy. 
4. CLASSIFICATION AND ACCURACY 
ASSESSMENTS 
Object-based segmentations were tried using different scale 
parameters (see Table 1). As can be realized that the smaller 
scale increases the dimensionality and dividing the object into 
the sub-groups, while the larger scale combines the multi- 
segments into one (see Fig. 3). 
  
  
  
  
  
  
  
  
  
   
  
  
  
  
  
  
  
  
  
  
  
Level 1 2 3 4 5 
Scale par. 5 10 16 25 250 
Color 0.7 0.5 0.4 
Shape 0.3 0.5 0.6 
Smoothness 0.9 0.9 0 
Compactness 0.1 0.1 1 
Seg. mode normal | normal | Spect. Diff. | normal 
  
  
  
Table 1. Segmentation parameters used for image 
  
EE 
Figure 3. Image segmentation using five different scale 
parameters. Scale parameter A = 5, B = 10, C = 16, 
D = 25, E = 250