133 
L 
L 
km. L § low 
;velopment of the 
10ut the growing 
:ypes. Although 
Intensity scales, 
se measurements, 
efficient of the 
le images and 
neasurements, 
late and in the 
factor was applied 
:e 4 is for 
Lng angle. The 
) the calibration 
lie. The radar is 
ilibration. The 
> preprocessing 
care of the 
irrection and 
calibration 
letter, as was 
:ors. 
i large contrast 
Figure 4. Development of radar backscatter throughout 
the growing season for some croptypes. X-band SLAR, 
horizontal polarization, 15° grazing angle. 
exists between winterwheat and the other croptypes in 
April and May. In June the contrast is very small, 
while all the crops are in their growing stage. In 
July a good contrast is present between all the crop- 
types, whereas in August the development of the 
backscatter coefficient of potatoes interferes with 
the one for winterwheat. 
The large contrast between winterwheat and the other 
croptypes in the early growing season only exists at 
low grazing angles. It can be explained as follows: 
the wintercrops, like winterwheat, are planted before 
winter and start growing in this area in April. The 
other croptypes are planted in April and May and show 
their biomass not before the end of May. Although the 
ground coverage by the new plants is small, the 
backscatter at low grazing angles is increased, 
because the smooth soil alone gives a very small 
amount of backscatter at these angles, so the small 
leafs sticking out of the ground contribute consider 
ably to the total backscatter. At larger grazing 
angles say around 40°, the backscatter from the fields 
is much increased and the previously described effect 
is smaller, resulting in very little to no contrast 
between these croptypes. 
Thus we should be able to distinguish between 
winter- and summer crops from one flight in April or 
May, and since our testarea contains mainly one 
wintercrop, namely winterwheat, we should be able to 
identify all winterwheat fields. Figure 5 shows the 
histogram of the field averaged radar backscatter 
coefficients of the SLAR image from May. From this 
figure it is clear that the winterwheat fields can 
be completely separated from the other fields, simply 
by applying a threshold level. 
Now that the winterwheat is identified, we must try 
to classify the remaining fields from other flights. 
This demonstrates the hierarchy in our classifier in 
contrast with the previous classification experiment 
(Ref. 1) where the time dependence of the radar back 
scatter throughout the growing season was used as 
discriminator. 
Sofar the design of the classifier was straight 
Figure 5. Histogram of the May flight: winterwheat 
(right) is separated from the other croptypes. 
forward and rather simple. However to derive an 
optimum result more elaborate methods should be used 
to investigate the data. Our main purpose is to make 
a selection from the available features per field. 
Eigenvalue or principal component analysis can be used 
to reduce the dataset into a set of uncorrelated 
features. This is done by a dataprojection on two or 
more Eigen vectors, which are determined from the 
covariance matrix of the dataset. 
An evaluation of the dataset using this method 
showed that the first two eigen vectors contained 
91% of the total variance, which means that the other 
four eigen vectors may be deleted. The first eigen 
vector is mainly determined from the April- and May 
features, whereas the second eigen vector is in fact 
a combination of the two July features, so the two 
flights at different altitudes. 
Since the datasets from April and May are highly 
correlated (correlation coefficient 0.91), the dataset 
of May was chosen as before and furthermore we 
selected the two July features. Figure 6 shows feature 
space plots for May versus July and for the 2 July 
features. A cross reference of the labels used in this 
and other figures can be found in Table 1. In both 
plots clusters of croptypes can be distinguished. 
A projection on one of the axes makes the classes 
inseparable, except for the wheat in May of course and 
the sugarbeets in July. The combination of the two 
July features means that we deal here with angular 
dependences to obtain discrimination. The short time 
interval between these two measurements more or less 
guarantees that the differences are only caused by 
the change in incidence angle. Therefore the clusters 
croptype 
label 
label 
potatoes 
sugarbeet 
winterwheat 
peas 
onions 
oats 
winterbarley 
beans 
grass seed 
spinach 
A 
B 
T 
E 
U 
H 
GR 
BO 
GZ 
SP 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
Table 1. Legend to plotlabels