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Remote sensing for resources development and environmental management
Damen, M. C. J.

Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986
Identifying agricultural crops in radar images
Physics and Electronics Laboratory TNO, The Hague, Netherlands
ABSTRACT: In 1980 a large SLAR flight program was carried out over an agricultural area in The Netherlands. A
classification study on this multitemporal dataset (Ref. 1) showed that high accuracies are obtained from a
simultaneous classification of 3 flights. In this paper the results of a follow-on study will be discussed. The
goal is to obtain the best possible classification result in the earliest possible stage of the growing season.
Therefore the SLAR flights from April, May, June and July were analyzed and the hierarchic classifier is intro
duced. Very satisfying results were obtained from a combination of 3 flights: 1 in May, 2 in July at different
incidence angles.
In this paper the results will be discussed from a
follow-on study on previous classification experiments
(Refs. 1, 3). This study forms a part of a broader
national remote sensing research program for agricul
ture and forestry, carried out by the ROVE-team (Radar
Observation on VEgetation), a collaboration of several
institutes (Ref. 1).
The testsite on which the study is performed is
situated in the Flevopolder, a reclaimed land area.
Figure 1 shows a part of this polder, including the
testarea. The latter contains 195 agricultural fields,
of which 164 were suitable for this experiment (crop
type known, reasonable dimensions). Frequently used
crop types in this area are winterwheat, potatoes and
sugarbeets (80% of total area). Onions and peas are
also important crop types, but grown on smaller fields
and therefore make up only 8.5% of the area. These 5
most occuring crop types were used in designing the
Figure 1. Map of the Flevopolder with the testarea
indicated (near 'Biddinghuizen'). The map shows an
area of 35 x 35 km. The testarea measures 3.7 x 6.2
The area was imaged with an X-band SLAR system,
using digital recording, on 5 different dates
throughout the growing season. At each flight date
recordings were made from 3 different altitudes,
resulting in 3 incidence angle ranges, and from 2
opposite sides of the testarea. This flight campaign
resulted in a multitemporal and multiangular database
of the area. A selection of these flights is shown in
fig. 2. The development of the radar backscatter
through time can be viewed from this selection. The
sampling interval is appr. 1 month. For July two
images are shown: one is flown at 660 m altitude,
like the other images shown, which results in a
grazing angle range from 7.5° to 16° (right to left
in the images). The second July image is flown at
1600 m, resulting in grazing angles between 18° and
For comparison a croptype map is shown in figure 3.
This figure results from the radar images, after
registration and field segmentation. The segmentation
is done manual by drawing the field boundaries in the
image on an image processing system. Only the 3 main
croptypes could be indicated here, because of the
limited separability of gray tones in a black and
white image after reproduction. However 80% of the
area is covered by these 3 croptypes.
The advantage of field segmentation of the radar
data is two fold:
1. The influence of speckle on the classification
result is reduced to practically zero. This also
holds for small inhomogeneities within the fields.
2. The amount of data is tremendously reduced,
since we end up with one value per image for every
field, thus 164 vàlues for one image.
For the classification experiment the radar data
of the 6 mentioned images were combined with the
groundtruth into one datafile. For every field there
are 7 features, i.e. the true field label (croptype)
and 6 average backscatter coefficients.
The purpose of the experiment was to design a
classifier on basis of the radar data of the 5 most
important croptypes. The classifier should be able
to distinguish between these croptypes as early as
possible in the growing season. This is different from
the previous experiment (Ref. 1), where we used the
flights of June, July and August for classification.
For operational applications an early result would be
much more useful. Certainly, an improvement over the
older experiment should be possible, considering the
high contrast in the early season flights (fig. 2,