4 INVESTIGATIONS 
The investigations are carried out on the image processing 
system of the Department of Photogrammetry and Carto 
graphy of the Technical University of Berlin, where soft- 
ware-packages for radiometric and geometric image pro 
cessing as well as for multispectral classification are 
implemented. 
For the investigations a part of the Landsat-scene 
210/26 from 9. August 1975 is used. This part covers a 
region of Baden-Württemberg, with the Oberrheingraben 
and the cities Mannheim and Karlsruhe. The seven most 
frequented classes in the concerned area should be dis 
tinguished with the conventional maximum-likelihood 
classifier. 
The following case-studies, which are also presented in 
Figures 1-4, are carried out: 
Comparison of classification results of original and 
contrast enhanced data 
- Comparison of classification results of original data, 
rectified and resampled with the methods of nearest 
neighbour and bilinear interpolation 
Comparison of classification results of contrast 
enhanced data, rectified and resampled with the 
methods of nearest neighbour and bilinear inter 
polation 
Comparison of classification results of contrast 
enhanced data; on one hand, row- and column-doubled 
before rectification and resampling (bilinear inter 
polation), and on the other hand without doubling 
before geometric processing 
ence will be expected between the classification results, 
because the histogram stretching affects only a linear 
extended distribution of the grey values, without any loss 
of information. 
Original Contrast En- 
Class 
Data 
hanced 
1 Water 
1.13 
1.13 
2 Coniferous F. 
20.40 
20.38 
3 Deciduous F. 
12.99 
12.84 
4 Urban areas 
8.44 
8.69 
5 Agriculture 
37.45 
36.35 
6 Grassland 
8.77 
8.73 
7 Vine 
10.82 
11.88 
Table la. Classified pixels in each class in (%) 
In Table la it can be seen, that there are only few 
differences in the distribution of classes in both images. 
Table lb is much more distinct for a comparison of 
classification results than Table la, because the exchange 
of pixels from one class to another is pointed out. In this 
Table the classification result of original data was used as 
reference image. The percentages in the lines show the 
class changing of pixels in the classification result of the 
contrast enhanced data. 
Data Contrast Enhanced 
Type 
For all rectifications the same transformation polynomials 
were used. The coefficients were evaluated considering 
the coordinates of nine control points. In all cases the 
training areas for the classes were defined in a colour 
composite of the contrast enhanced data. Subsequently the 
edge coordinates of the training areas, which have been 
obtained during definition, were calculated for the respec 
tive preprocessed data. These new coordinates could be 
used to obtain the features (grey-values) of the rectified 
and resampled data. This method guarantees comparison of 
the results, because all classifications use statistics de 
rived from the same training areas. However the sites 
differ through their features and/or the number of features 
when contrast enhancement, rectification and resampling 
is carried out. 
Class 
1 
2 
3 
4 
5 
6 
7 
1 
99.49 
0.13 
_ 
0.35 
_ 
0.02 
0.01 
2 
0.01 
99.70 
0.12 
0.06 
- 
- 
0.11 
3 
- 
0.29 
98.44 
- 
- 
0.09 
1.17 
4 
0.01 
- 
- 
99.39 
0.09 
- 
0.51 
5 
- 
- 
- 
0.72 
96.95 
0.50 
1.83 
6 
- 
- 
0.32 
- 
0.43 
97.25 
2.01 
7 
- 
- 
- 
0.12 
- 
- 
99.88 
Sum of pixels : 1048567 
Percentage of pixels classified the same : 98.28 % 
Table lb. Pixel by pixel comparison of classification results 
of original and contrast enhanced data 
4.1 Classification Results of Original and Contrast 
Enhanced Data 
This case-study compares the classification results of orig 
inal and contrast enhanced data (Figure 1). For contrast 
enhancement of the used part of the scene a linear 
histogram stretching was carried out for each channel 
separately. The result is more appropriate for visual inter 
pretation particulary for defining training areas. No differ- 
Figure 1. Comparison of classification results of radiome- 
trically preprocessed data 
In Table lb the greatest differences occur in the classes 
deciduous forest, agriculture and grassland. The mis- 
classified pixels are assigned mainly to vine. Visual inter 
pretation of the investigated part of the scene shows, that 
misclassifications appear in areas, which are highly struc 
tured. The creation of a difference image confirms the 
visual comparison of both classification results. In homoge- 
nious regions like those covered with coniferous forest and 
in greater parts of decidous forest and agriculture no 
differences can be detected. 
The reasons for misclassification can be explained by the 
spectral reflectance of the special classes and the contrast 
enhancement. It is very difficult to define training areas in 
highly structured regions. Such training sites may also 
contain grey-values, which are not characteristic to the 
considered classes. So the variance of such classes is 
relatively high. The contrast enhancement will affect more 
in increasing of the variance of highly structured classes, 
than that of homogenous classes. Another reason can be 
explained by the histogram stretching for every channel 
separately. This influences each channel differently, which 
results in different covariances for original and contrast 
enhanced data. By using a conventional maximum-likeli 
hood classifier these facts effect a change of assigning 
pixels to such classes. 
By means of these facts we can conclude, that differ 
ences between classification of original and contrast 
enhanced data exist, but they are usually small. The 
percentage of pixels, which are assigned to the same 
classes in both images, amount to about 98 %.