134
JUL GflMMR IN DB JUL 1600 GOMMA IN DB
-
T
1
R
2
U
3
E
4
L
5
6
A
7
B
8
E
9
L
10
Table 2. C
segmentati
of labels)
Figure 6. Feature space plots of May vs July and the 2 July features (low and high altitude).
are rather small. Even the winterwheat seems to be
separable in this plot, but since this can be done in
May, no further attention is paid to it. To reduce the
dataset, we now introduce a linear combination deter
mined as the first eigen vector of the two July
features, to optimise the separability of the crops.
Figure 7 shows the plot of May versus the combina
tion of the July features. Figure 8 shows a histogram
of the July data projected on the new axis. Winter-
wheat fields are excluded in this histogram. The peaks
are from left to right potatoe, peas, onion and
sugarbeet. The classes can be separated with the
parametric Bayes classifier for normal distributions.
A test of the designed classifier on the same data
as used for the design, produced a very high
classification result, which is not surprising.
However since we have no other data available, it is
difficult to test the classification algorithm.
Figure 7. Plot of May versus the projection feature
(linear combination of the 2 July features).
Figure 8. Histogram of the projection feature. Peaks
are (left to right) potatoe, peas, onion and
sugarbeet.
To perform some sort of test, the data from fig. 2
was automatically segmented using a split and merge
algorithm (Ref. 5) and then classified. This brings
a little variation in the data, because the field
boundaries now differ from the ones in the manual
segmentation. Of course, this is only a small effect,
therefore care should be taken in the interpretation
of the classifier results. Table 2 shows these
results. The first 5 classes were used for optimizing
the design of the classifier. Classes 6-8 represent
a very small amount of data and cannot be considered
to be representative.
Class 8 (b
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