43 
.ssification results, 
;cts only a linear 
s, without any loss 
Contrast En 
hanced Data 
1.13 
20.38 
12.84 
8.69 
36.35 
8.73 
11.88 
n (%) 
lere are only few 
es in both images. 
a comparison of 
ause the exchange 
pointed out. In this 
.1 data was used as 
:he lines show the 
ation result of the 
need 
5 6 7 
- 
0.02 
0.01 
- 
- 
0.11 
- 
0.09 
1.17 
0.09 
- 
0.51 
>6.95 
0.50 
1.83 
0.43 
97.25 
2.01 
- 
- 
99.88 
: 1048567 
le : 98.28 % 
assification results 
:cur in the classes 
issland. The mis- 
vine. Visual inter- 
: scene shows, that 
h are highly struc- 
nage confirms the 
'esults. In homoge- 
niferous forest and 
nd agriculture no 
De explained by the 
;s and the contrast 
ne training areas in 
lg sites may also 
iracteristic to the 
>f such classes is 
nt will affect more 
structured classes, 
her reason can be 
for every channel 
differently, which 
ginal and contrast 
il maximum-likeli- 
lange of assigning 
elude, that differ- 
inal and contrast 
isually small. The 
;ned to the same 
?8 96. 
4.2 Classification Results of Rectified and Resampled 
Original Data 
The proceeding of data processing for a comparison of 
original and rectified and resampled data is shown in 
Figure 2. It is very difficult to compare classification 
results of original and geometrically preprocessed data, 
because rectifying the images causes another scale and 
another form. Therefore comparison is only possible by 
studying the classified images and the percentage distribu 
tion of classes in the whole image. 
Figure 2. Comparison of classification results of geo 
metrically preprocessed data 
In Table 2a it can be seen, that classification results of the 
original and nearest neighbourhood resampled data differ 
only slightly. A visual examination confirms this result. 
The classification result of resampled data with bilinear 
interpolation shows greater differences compared to that 
of the original data. The apparent differences are caused 
by geometric transformations, which create more or less 
new pixels between the rectified pixels. 
Original 
Original Data 
Data 
Nearest 
Bilinear 
Class 
Neighbour 
Interpolation 
1 Water 
1.13 
1.15 
0.87 
2 Coniferous F. 
20.40 
20.46 
20.47 
3 Decidous F. 
12.99 
12.99 
12.27 
4 Urban Areas 
8.44 
8.51 
8.66 
5 Agriculture 
37.45 
37.53 
38.94 
6 Grassland 
8.77 
9.01 
8.21 
7 Vine 
10.82 
10.35 
10.58 
Table 2a. Classified pixels in each class in (%) 
Because the classification of original and nearest neighbour 
resampled data are very similar, the following investi 
gations are carried out only with the rectified data. For 
this purpose we can refer to Table 2b which shows pixel to 
pixel comparison, using nearest neighbourhood resampled 
data as reference image. The Table shows great differ 
ences between the classification results. Only about 86% of 
the whole image part are classified the same. The altering 
can be explained by the method of bilinear interpolation, 
which uses for the generation of the new pixels the 
surrounding pixels, while the nearest neighbour algorithm 
only takes already existing grey-values. Especially a class 
changing of isolated pixels and other small areas, e. g. 
Data 
Original, 
Bilinear 
Interpolated 
Type 
Class 
1 
2 
3 
4 
5 
6 
7 
D 
O 
_Q 
1 
68.72 
5.84 
_ 
22.47 
0.27 
2.65 
0.04 
SZ 
—T DO 
2 
0.11 
92.72 
3.86 
1.12 
0.09 
0.04 
2.07 
S3 
3 
- 
8.41 
81.63 
0.03 
0.40 
4.35 
5.19 
4 
0.67 
1.46 
0.01 
83.15 
10.61 
0.01 
4.08 
ô jö 
5 
- 
0.01 
0.02 
1.74 
92.90 
2.28 
3.05 
6 
- 
0.02 
5.59 
0.02 
18.22 
71.54 
4.61 
<u 
z 
7 
- 
2.01 
3.55 
4.17 
14.14 
2.99 
73.15 
Sum 
of pixels 
: 
1915289 
Percentage of pixels classified the same : 86.32 % 
Table 2b. Pixel by pixel comparison of classification results 
of resampled data with nearest neighbour and bilinear 
interpolation. 
lines, borders, etc. can be recognized. Mainly the changing 
from riverbanks and lakesides to urban areas is evident. 
Moreover small areas situated in a larger region have a loss 
of space, while the greater surfaces increase. 
In the visual comparison of both classification results it 
can be seen that the result of nearest neighbour resampled 
data is more differentiated, but classification of bilinear 
interpolated data looks more homogeneous. The subtraction 
of both images from each other shows very distinctly the 
effects of bilinear interpolation to edges and lines. 
However only large areas with homogeneous spectral 
reflectances can be classified correctly (coniferious forest, 
some areas of agriculture), while highly structured regions 
will tend to more misclassification. An additional inspec 
tion of the feature space shows that changing of pixels 
tends mostly to adjoining classes. 
4.3 Classification Results of Rectified and Resampled 
Contrast Enhanced Data 
In this case-study for rectification and resampling the 
contrast enhanced data are used (Figure 3). It is expected, 
that classification results of this data show only small 
differences in comparison to the results of chapter 4.2, 
because the classification of the original and the contrast 
enhanced data also differ slightly. 
Figure 3. Comparison of classification results of radiome- 
trically and geometrically preprocessed data