view of the most frequent grey levels and their variance in all 
spectral bands for a specific test area and may be the basis for 
classification. The distribution shown in fig. 4 was taken on a 
relatively large window overlapping both land and water. One ob 
serves a bimodal distribution in the two infrared bands. The 
lower grey level peak in each of these bands corresponds mainly 
to water. A further use of the grey distribution is the possi 
bility of computing a greyscale which forces an equal distribu 
tion and thus enhances contrast without human manipulation. 
4.4. PRINTER PICTURES 
Although shapes can hardly be recognized in pictures printed out 
in numerical matrix form, the numerical values are needed for cer 
tain problem areas such as: reflectivity measurements, contrast 
measurements, comparision between ground measurements and remotely 
sensed data, calibration of thermal mapping. Fig. 5 shows a part 
of our test picture printed. 
4.5. TWO-DIMENSIONAL DISTRIBUTIONS 
Two-dimensional distributions bring light into the dark, where 
single grey level distributions do not reveal significant diffe 
rences. Fig. 6 shows the distribution of MSS 4 vs.MSS 6 written 
on photographic paper in the form of a 64 by 64 dot picture. The 
cluster corresponding to water is of course clearly separated 
from the valuesfor land, while the two different types of vege 
tation are not as clearly separated. 
Distributions using derived features can be still more helpful. 
The distribution of the ratios (MSS 4 - MSS 7) / (MSS 4 + MSS 7) 
vs, (MSS 5 - MSS 6) / (MSS 5 + MSS 6) for the window printed out 
in fig. 5 is shown in fig. 7. A variety of clusters can be de 
tected in this arrangement, which will not be distinguishable 
in the grey vs. grey distribution.