resented by rules. 
nted in the seman- 
relations exist ac- 
ition over the links 
à condition and an 
ew interpretation 
> net. If a situation 
action is executed 
ssed node accord- 
tected when all its 
instances, is repre- 
lete instance 
art—of(ng) } 
ymplete instance. 
rious sets of rules. 
or landscape mod- 
three conceptual 
escribes the scene 
sents the objects 
"The bottom layer 
pecific photomet- 
. If more than one 
plied accordingly. 
cabulary. E.g. the 
) — geometry layer 
jads, forests, and 
Lis composed of a 
be modelled sepa- 
has to be visible in 
ious stripes in the 
d2D— region’ and 
for segmentation 
es respectively. 
the objects of the 
es of the semantic 
its the knowledge 
| expected in the 
recognition three 
shed: hypotheses, 
ypotheses are not 
tances contain all 
parts. Complete 
ory parts and con- 
Layer 
part-of 
Vegetation 
  
  
  
  
  
  
  
  
  
  
  
  
  
| 
part-of : E part-of | 
| 
| Grassland | | Forest | | 
1 À part-of Ÿ cdpart-of | part-of 
| Forest Forest | Road 
| Roof Edge | Segment 
| 
3 3 
         
      
     
  
  
      
    
    
  
  
Wood Wood 
3D —Surface 
Grass 
3D-— Surface 
   
  
Textured 
2D— Region 
  
   
     
   
3D-Stripe 
Camera 
  
  
  
  
  
  
3 
    
        
   
      
    
  
  
    
    
Asphalt 
3D-Camera 
3D-Stripe : 
   
Homogenous 
2D) —S 
Fig. 4: Simplified semantic net representing prototypes of landscape objects 
Interpretation proceeds primarily top down. Hypotheses are 
propagated from the scene layer to the sensor layer to test 
them in the sensor data. The propagated hypothesis at the 
sensor layer calls methods for segmentation of textured re- 
gions or homogenous stripes respectively. The result of the 
verification is returned to the superior concept which consec- 
utively generates new hypotheses. 
If the verification returns competing instances each possible 
interpretation is analyzed separately. For this each possible 
interpretation is documented by a search node which con- 
tains all concepts with their current interpretation state. Each 
time competing interpretations occur the search node splits 
into child search nodes. The leaves of the resulting search 
tree represent the currently competing interpretations. To fo- 
cus interpretation on promising search nodes they are judged 
and ranked. The judgement computes the compatibility be- 
tween expected concept properties and found concept prop- 
erties by comparing the range and value slots of attributes. An 
A' Algorithm selects the best judged interpretation for fur- 
ther investigation. 
4.2 Segmentation 
The initial concepts in the sensor layer are instantiated by 
segmentation of images. Presently two different initial con- 
cepts with segmentation methods are available: the concept 
‘textured 2D —region’ and homogenous 2D —stripe’. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
4.2.1 Segmentation of Homogenous Stripes: The extraction 
of homogenous stripes is based on gradient filters for edge 
detection with consecutive conditional local ranking to en- 
hance weak contours. Thresholding yields a binary image in 
which candidates for homogenous stripes show up as long 
parallel lines. Figure 5a shows the segmentation result. 
4.2.2 Segmentation of Textured Regions: Forests and grassland 
of natural terrain are characterized by different textures. The tex- 
ture of a class k, e.g. forest, is assumed as generated by a station- 
ary ergodic process. The prerequisite of statistical independence 
allows to compute the probability P;(ylk) of the texture process 
from the luminance histogram of all intensities y in the learn re- 
gion. 
Generally the probability for occurrence of a luminance value is 
not independent from its neighbours. Hence a texture model of 
second order statistics is used. Local mutual dependencies can be 
modelled by Gibbs random fields. Couples of neighbored pixels, 
named cliques, are inspected. Three measures of co—occurrence 
are computed for each clique: 
— the probability P2(ylk) of common class membership, 
— the probability P3(ylk) of a luminance difference within a 
region, 
— the probability P4(ylk) of a luminance difference between 
different regions. 
Gibbs random fields describe the joint probability 
=> Vel) 
c 
Pkly) = ; Z = normalizing constant. (1) 
1 
7 © 
4 
Viylk) = = > MIn(P(ylk)) Q) 
i=l