CIPA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey 
300 
Pixel based classifications 
Object based classifications 
^ □ Pixel ^ 
Unsupervised 
Supervised 
classification 
classification 
Pixel 
< 
Multivariate 
▼ 
segmentation 
Label 
assignement 
Mini. 
distance 
Maximum 
likelihood 
Nearest 
Neighbour 
1 
r 1 
r 
1 
r 
Rules (object 
features + class 
related features) 
a) Isodata 
classification 
wrm 
li. 
b) Min. distance 
classification 
S J W : 
«û—ûfl? 
» 
c) Max. likelihood 
classification 
d) Nearest Neighbour 
sample based 
classification 
¿à-: 
e) Rule based 
classification 
Caption : 
□ tiled roof; HI oak wood beams; □ facing (masonry); El freestone; □ shutter; □0 sky 
Figure 3. Flow chart of all experiments and results achieved 
4.2. Rule-based classification 
In this part, a higher degree of image analysis is applied. The 
most delicate and also interesting work consists in developing 
rules describing and formalising the knowledge on 
characteristics of Alsatian façades. These rules can integrate 
various object features (spectral values, shape, texture, 
hierarchy, etc.) or class related features (relation to neighbour 
objects, sub-objects, etc.) which are furthermore defined by 
membership functions. In these membership functions one 
can introduce fuzzy logic by particularly weighting that 
feature and combining it through logical operators with other 
features. 
For instance, to discriminate sky from the rest of the image, 
one use the particularity that sky brightness is stronger than 
the brightness of the rest of the image. Another characteristic 
is that the standard deviation of the sky is especially small. 
Result is shown in Fig.4. 
Figure 4. Rules established for the discrimination 
of the class “sky” (blue)