44 
By examining Table 3a it is apparent, that only a little 
change in the distribution of each class in respect to the 
classification result of the original data, has taken place. 
This is especially evident for data, which is resampled with 
the nearest neighbour algorithm. Moreover the classifica 
tion results of the bilinear interpolated data seems to have 
only small deviations relative to the original data, but 
visual interpretation shows significant differences. 
Original 
Class 
Data 
1 Water 
1.13 
2 Coniferous F. 
20.40 
3 Decidious F. 
12.99 
4 Urban Areas 
8.44 
5 Agriculture 
37.45 
6 Grassland 
8.77 
7 Vine 
10.82 
Contrast Enhanced Data 
Nearest Bilinear 
Neighbour Interpolation 
1.15 
1.04 
20.43 
20.78 
13.06 
12.30 
8.61 
9.05 
37.36 
37.61 
8.99 
8.45 
10.40 
10.76 
Table 3a. Classified pixels in each class in (%) 
In order to investigate the exchange of pixels assignment 
to classes of the differentially resampled data, Table 3b, 
using data resampled with the nearest neighbour method as 
reference image, is referred. Generally this Table shows 
the same tendencies as Table 2b, but with 89% pixels 
classified the same, the difference in classification results 
between the resampling algorithms is smaller than in 
Table 2b. Mainly the agreement of water pixels increases 
remarkably. 
Data Contrast Enhanced, Bilinear Interpolated 
Type 
„ Class 12 3 4 5 6 7 
Figure 4. Comparison of classification results of doubled 
and not doubled data, radiometrically and geometrically 
preprocessed 
u 
c 
o 
jQ 
1 
83.34 
6.74 
- 
9.88 
- 
rtf 
jC 
XL 
60 
2 
0.12 
94.69 
3.34 
0.53 
- 
C 
LLÎ 
’5 
3 
- 
8.62 
84.06 
0.02 
0.05 
•M 
z 
4 
0.63 
0.94 
0.01 
88.88 
5.26 
rtf 
00 
d) 
5 
- 
- 
- 
1.95 
93.56 
+-* 
C 
L. 
6 
- 
0.02 
4.58 
0.01 
13.51 
o 
O 
<D 
z 
7 
- 
1.48 
2.19 
4.29 
9.45 
Sum of pixels : 
Percentage of pixels classified the same : 
_ 
0.03 
Contrast 
Rectified Data 
_ 
1.31 
Enhanced 
None- 
Doubled 
3.37 
3.87 
Class 
Data 
Doubled 
0.03 
4.26 
1.81 
2.69 
1 Water 
4.22 
4.59 
4.03 
77.81 
4.07 
2 Coniferous F. 
9.12 
9.06 
9.02 
3.22 79.39 
3 Deciduous F. 
8.89 
7.90 
8.25 
4 Urban areas 
28.66 
28.05 
28.54 
1915289 
5 Agriculture 
34.82 
34.16 
34.90 
89.13 % 
6 Grassland 
14.29 
16.01 
15.04 
Table 3b. Pixel by pixel comparison of classification results 
of resampled contrast enhanced data with nearest neigh 
bour and bilinear interpolation. 
4.4 Classification Results of Contrast Enhanced Data, 
Doubled and None-Doubled before Rectification 
Classification of rectified data with resampling of nearest 
neighbour delivers results with only less accuracy loss. But 
if classification of resampled data with bilinear inter 
polation is carried out the results will be of unsatisfying 
accuracy. In order to obtain better classification accuracy, 
the experiment of doubling the input data before rectifica 
tion is carried out (Figure 4). 
Considering the available disk memory only a little part 
of the so far investigated region is choosen. Consequently 
new training areas for now six classes are defined. Subse 
quent classification of contrast enhanced original data and 
rectified doubled and none-doubled data is executed. Table 
4 shows the percentual distribution of classes in the 
results. 
It can be pointed out that the classification outcome of 
rectified data with doubled input is much more similiar to 
the outcome of geometrically none-processed data. In 
studying the results visually, better agreement of these 
data with the original data can be recognized, especially in 
lines and edges. An additional evaluated difference image 
shows mainly distinctions in riverbanks, lakesides and edges 
Table 4. Classified pixels in each class in (%) 
of greater areas. Also isolated pixels, which disappear in 
normally rectified data, occur in the result of doubled 
data. Consequently this shows that doubling of data before 
rectifying produces an obvious improvement of classifica 
tion accuracy. 
5 CONCLUSIONS 
It can be concluded that the various kinds of data prepro 
cessing before classification influence the results. The 
main results of the above investigations are briefly dis 
cussed. They underline the different influences of the 
various preprocessing methods. 
Classification of original and contrast enhanced data 
shows only small differences, if linear histogram 
stretching to each channel is carried out separately. 
Classification results of nearest neighbourhood re 
sampled data show only little change in respect to 
the results of original data. This is valid for re 
sampling of original and contrast enhanced data. 
Classification results of bilinear interpolated data 
show great differences in respect to the result of 
original data. Most of the changing occurs in isolated 
pixels, lines and edges. Therefore classes with a lot 
of I 
ing. 
tior 
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- Cla: 
lati' 
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The above 
influence: 
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of neare; 
which is * 
great gee 
map grid) 
If bilim 
fication r 
those of t 
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if a doub 
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In the \ 
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of image 
REFEREi' 
Forster, E 
Landsa 
Photo. 
Kahler, M 
ly Pro 
Mappin 
1986 (ir 
Realnutzi 
1:5000C 
Neckar 
Frankfi