enhancement techniques can facilitate and improve photointerpretation dramatically. 
2. Supervised classification requires considerable ground information for specific areas 
to select representative samples for the classes, which determine the quality of the 
results, It is therefore not suitable for unknown areas. The classification itself is fast 
even for vast areas and the results in digital form can easily be incorporated in a data 
base or in model calculations. 
a 
Unsupervised classification stands between supervised classification and photointerpre- 
tation. The results are also available in digital form. Ground information is not a 
prerequisite but the clusters are to be interpreted using more or less extensively photo - 
interpretation techniques. It can be regarded as a sophisticated enhancement technique, 
also displaying small classes, which may get overlooked with standard enhancement 
techniques including ratios or principal component transformation . 
A 
The classification techniques are restricted to identification of object classes and 
to a limited statistical analysis, e.g. acreage determination. Further conclusions or 
deductions must be made by the analyst or additional evaluation methods e.g. model 
calculations. The photointerpreter can incorporate some of these processes in his inter- 
pretation procedure and is therefore in a better position to direct his efforts to the 
information directly related to the task he has to solve. For some problems the best 
solution would be to have the same person do the interpretation and further evaluation. 
COMMON PROBLEMS 
  
The analysis of the different procedures shows also that there are many problems common 
to all techniques. The most important set of problems regards the determination of 
characteristic features to separate the classes. In photointerpretation often a key is used 
with qualitative descriptions of features e.g. brightness, colour, texture, shape, size, 
pattern characterizing the different classes. In classification similarly quantitative 
statistical values, e.g. means and covariance matrices of the brightness values in different 
channels, must be determined. Experience in photointerpretation shows that some of the 
features may change considerably even within a frame, e.g. systematic brightness or 
color changes due to the relative position of the sun. Other features, e.g.the linear, 
bending form of rivers, are similar in very distant places. 
Comparable conditions are valid with classification techniques. In airborne scanner data, 
the brightness change due to scan angle is very pronounced and deteriorates classification 
10 
acc 
con 
whe 
In c 
are 
cha 
sen: 
to c 
. but 
val 
ano 
trul 
Fur 
dat 
stai 
Cor 
the 
anc 
nor 
wit 
int 
cla 
So 
to | 
exp 
als 
col 
the 
the 
To 
see 
=