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IV  Supervised classificat ion 
Using the described method we have overlaid 5 Landsat subframes of the same 
year. As next step we have classified the pixels of this 20 channel picture 
by linear stepwise discriminant analysis (3). In this way we hope to be 
able to distinguish each of the given land use categories within the test 
site "Grosses Moos". 
Misclassifications have to be taken into account by picture-elements of 
smaller fields than the resolution and by pixels lying on the border to a 
neighbouring field. Since a viewing field is cut by scanlines in a different 
manner with each overflight of the satellite quite a large number of mixed 
pixels are to be expected. 
The following categories were classified (see also Table 1): 
lake, reed, urban land, woods, barley, summer wheat, winter wheat, rye, 
potato , sugar beet, rape seet, rape, vegetables, pea, meadowland, corn 
and vine-yards. 
A visual impression of the classified test site is given in Fig. 2 where the 
character for each category (code see Table 1, column 2) is printed image- 
wise. For easy comparison we have masked those pixels (white areas) where no 
ground truth was available. 
As a technical detail and example a summary of computational results is 
given in Table 2. This was one output of the stepwise linear discriminant 
analysis for the 20-channel problem: from this table we can read out the 
priorities for the "variables entered" ‘in the routine whilst computing. 
Unfortunately there is no physical intuition for understanding this result - 
we only know from experience that if we change one of the training sets a 
little bit, the hierarchy will change completely. 
V" Graund truth digitization 
To verify the classification results it is necessary to compare them with 
ground truth information. Instead of trying to match visually a satellite- 
picture with maps or orthophotographs (e.g. with a zoom-transfer scope), in 
order to identify the fields, we have tried a digital method for picking out 
samples: 
We digitized a ground truth map with the same precision as that of the 
Landsat scenes. That means that we have in the end a ground truth pixel 
for direct comparison with each classified Landsat pixel. Naturally our 
ground truth does not cover the test site completely. 
Fig. 3 shows a character-print of the ground truth signatures where each 
capital represents a feature corresponding to the codes given in Table 1, 
column 2.