4 DATA ACQUISITION
5 PREPROCESSING
In order to ensure optimum effectiveness of an air
borne campaign great attention is paid to internal
and external calibration of the radar and choice of
flight dates and -geometry.
4.1 Calibration
Early in 1985 the Dutch digital SLAR was fitted with
an internal calibration mechanism. In short, the
procedure may be visualized as follows. A small part
of every transmitted pulse is delayed and weakened,
then positioned at the beginning of each received
backscatter signal. From the amplitude of the
recorded calibration pulse the amplitude of the ori
ginal, transmitted pulse is calculated. Hence each
received and recorded radar return signal may be
corrected for variations in the corresponding trans
mitted pulse.
As an additional measure of validating the calibra
tion external corner reflectors are placed in the
target area. From the raw received signals the
return of the corner reflectors may be calculated
and compared with the theoretical reflection from a
well-defined corner reflector.
It is quite necessary for further processing and
classification to start out with a calibrated system
as the transmitted power of the X-band SLAR is ex
pected to fluctuate with elapsed time.
4.2 Optimization of flight-date and -geometry
The choice of the optimum flight days and the deter
mination of the flight geometry is essential in
planning an airborne campaign. The main purpose is
to identify dates and angles whereby the possibility
of discriminating various crops is the greatest.
When identifying the ROVE ground truth data base it
was found in 1983 and 1984 that an airborne SLAR
campaign was to be conducted between May and August,
as potatoes do not appear above the soil until late
May and may be harvested as early as late July in
The Netherlands. Furthermore the data base and crop
growth parameters showed that winter wheat and
potatoes tend to exhibit similar backscatter charac
teristics in all months except May and that potatoes
plus winter wheat can be discriminated from most
other crops in July (Figure 2, 3). Thus, in 1983 and
1984, it was decided to conduct an airborne campaign
with the following objectives:
- distinguish potatoes from winter wheat in May and
- distinguish potatoes from other crops in June/
July.
The above objectives could only be achieved with
specific viewing angles. Thus an aircraft altitude
of 500 meters above ground to obtain 5°-14° and
another run at 2700 meters above ground to enable
25°-45° grazing angles were deemed necessary.
In 1985 the ROVE group "Crop Identification" chose
to adhere more to expected satellite programmes,
where steeper angles for satellite simulation are a
prerequisite.
Again the ROVE data base was consulted and this
resulted in a flight altitude of app. 4000 meters in
order to obtain grazing angles of 40°-70° for one
track and 30°-48° (grazing) for a laterally trans
posed track at the same altitude (Fig. 4). The
corresponding appropriate flight dates of early June
and early July 1985 gave good confidence to be able
to adequately discriminate between the various crop
types.
In order to enable steep grazing angles the SLAR
antenna had to be rotated by almost' 20° in order to
make use of the linear part of the antenna gain
function [Ref. 6, 7].
To overlay and register data from multitemporal air
borne sensors may be very difficult due to random
variations for attitude, velocity and position of
the aircraft during the execution of the flight
track. These deviations usually distort the resul
ting data set sufficiently to disable further multi
temporal processing. Also, several effects influence
the signal received by the radar antenna; e.g. the
antenna gain is a function of the depression
angle, the received signal strength is dependent
on slant range distance. Hence, the National Aero
space Laboratory NLR in close collaboration with
the Physics and Electronics Laboratory TNO, the
Delft University of Technology and the Survey
Dept, of Rijkswaterstaat, undertook to implement a
preprocessing procedure (PARES) on the central
computer of NLR. PARES enables raw acquired data
to be corrected for variations in aircraft motion
and -attitude through simultaneously recorded INS
parameters in the NLR laboratory aircraft (INS =
Inertial Navigation System). For every acquired
line its exact position in a terrestrial
coordinate system is calculated, thus enabling
later multitemporal registration of data via an
earth-bound coordinate system. The PARES-procedure
also accounts for radiometric system corrections.
The data preprocessed through PARES may now be
multitemporally processed without further complica
tions due to airborne data acquisition [Ref. 2, 3].
6 PROCESSING AND CLASSIFICATION
The inherent speckle of a radar image is a distur
bing factor in further processing and classifi
cation. Also, most advanced classification algo
rithms and user-defined information extraction pro
grammes do not require information on a pixel-by-
pixel basis but rather on a field-by-field basis.
Many microwave remote sensing groups have devised
ways to minimize speckle through field averaging.
Various filtering algorithms have been tried but The
Netherlands ROVE-group has chosen for the "split-
and-merge" algorithm based on the Pavlidis-concept
which has been implemented on the image processing
system RESEDA at the National Aerospace Laboratory
NLR [Ref. 5].
The algorithm has been designed to process (in
parallel) up to 7 multitemporal images with a number
of user-identified parameters like minimum field
size and variance to obtain field averages where
original pixel-values have been replaced by a field-
dependent mean value without loss of information.
Care must be taken to exclude fields which contain
only a few pixels as they are likely to contaminate
the segmentation result. Other small objects like
power-line masts, farms, etc. will also corrupt the
resulting data set [Ref. 4, 5].
After this extensive (pre-)processing the final
classification procedure can be comperatively
simple. An interactive parallelepiped classifier may
be applied to the data set. The classifier allows
for interactive classification from data space into
feature space and vice versa. It will be obvious
that this procedure is usually very time-consuming
when applied to standard imagery. The preceeding
segmentation algorithm has, however, already greatly
reduced the dynamic range of possible pixel values,
so now near real-time classification is possible.
Great care must be taken, to prevent the angular
dependency of backscatter of agricultural crops to
be introduced into the classification result. One
way to eliminate this effect is to process suffi
ciently narrow strips where the angular dependence
may be neglected.
