20 24 
IN DB 
feature. Peaks 
ion and 
:a from fig. 2 
¡plit and merge 
:d. This brings 
lse the field 
.n the manual 
r a small effect, 
i interpretation 
lows these 
;d for optimizing 
>6-8 represent 
)t be considered 
C 
L A 
S S 
I F 
I E 
R L 
A В 
E L 
1 
2 
3 
4 
5 
6 
7 
8 
T 
1 
33 
1 
1 
R 
2 
36 
U 
3 
53 
E 
4 
1 
1 
5 
1 
5 
2 
1 
1 1 
L 
6 
3 
1 
A 
7 
3 
В 
8 
1 
4 
E 
9 
3 
1 
1 
L 
10 
1 
Table 2. Classification result after automatic field 
segmentation of the testarea (see table 1 for legend 
of labels). 
REFERENCES 
Hoogeboom, P. 1983. Classification of Agricultural 
Crops in Radar Images. IEEE Trans GRS Vol GE-21, 
p. 329-336. 
Hoogeboom, P.; Binnenkade, P. and Veugen, L.M.M. 1984. 
An algorithm for radiometric and geometric 
correction of digital SLAR data. IEEE Trans GRS 
Vol GE-22, p. 570-576. 
Smit, M.K. 1979. Preliminary results of an investi 
gation into the potential of applying X-band SLR- 
images for croptype inventory purposes. IEEE Trans 
Geosci Electron Vol GE-17, p. 303-308. 
Loor, G.P. de; Hoogeboom, P. and Attema, E.P.W. 1982. 
The Dutch ROVE Program. IEEE Trans GRS Vol GE-19, 
p. 3-7. 
Gerbrands, J.J.; Backer, E. 1983. Segmentation of 
multitemporal side-looking airborne radar (SLAR) 
images. Proceedings SPIE volume 397, p. 173-179. 
(Geneva, April 19-22) Applications of digital 
image processing. 
Gerbrands, J.J. 1982. Introduction to digital image 
processing (in Dutch), lecture notes L73A, Delft 
University of Technology, November. 
Leeuwen, P.J. van, 1984. Analysis of SLAR images 
(MSC thesis, in Dutch), Delft University of Tech 
nology, Information Theory group, July. 
Class 8 (beans) is not planted until July, so in July 
these fields are still almost bare, and therefore 
easy to recognize (see fig. 2). Classes 9 and 10 are 
not considered in the classifier and therefore 
identified as other croptypes. 
3 CONCLUSIONS 
In this paper a follow-on study into the possibilities 
of crop identification was presented. The goal was to 
improve the classification result from a previous 
study by adding early season SLAR flights and to 
enable crop identification as early as possible in the 
growing season. 
A hierarchic classification procedure is proposed. 
The success of this classifier is based on the 
separability of winterwheat or rather wintercrops at 
low grazing angles (5° - 15°) in the early growing 
season (April, May) and the ability to discriminate 
other croptypes in the mid-season on basis of their 
angular dependence in the grazing angle range 5° - 
35°. Field averaged radar backscatter values are used. 
The test of the classifier was performed on the same 
dataset as was used for the design of the classifier, 
although for the test the fields were segmented in a 
different way (automatic instead of manual). Care 
should be exercised in the interpretation of the test 
results, since the success percentages may be over 
estimated in this situation. 
Further investigation should incorporate a test in 
ecologically different areas and areas with different 
and more varied crop distributions. Also the use of 
angular dependence should be further investigated. In 
the Netherlands a research project is running to cover 
these subjects. 
4 ACKNOWLEDGEMENT 
This study has been perfprmed by members of the ROVE- 
team. Part of it was financed by the National Remote 
Sensing Steering Committee (BCRS). ' 
Contributions to the work at the Physics and 
Electronics Laboratory TN0 The Hague came from: 
National Aerospace Laboratory NLR in Amsterdam, image 
collection, Centre for agrobiological Research (CABO) 
in Wageningen, ground data collection, Information 
Theory Group of the Delft University of Technology, 
classification. 
Mr. R. Vlaardingerbroek of the Physics and 
Electronics Laboratory TNO is credited for the many 
calculations and plots he produced on the subject.