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Fig. 4 Rollei 6006 metric 
Due to the fact that there was no a-priori knowl- 
edge about the spectral behavior of the different 
materials, no special part but a wider range of 
the spectrum was selected. 
  
  
F C4 
    
20 
1 
de 
S00 700 900 2 [nml 
  
  
Fig. 6 Transmission curvature of MKF6 filters 
(from [Kronberg 1985]) 
In general, metal interference filters taken from 
the multispectral camera MKF6 of Carl Zeiss Jena, 
     
  
  
  
  
    
   
  
  
  
  
   
  
    
  
  
  
  
   
Fig.5 Rollei RS1 Réseau-Scanner 
which mainly had been developed for remote 
sensing in geology, were used, replenished by 
some ORWO gelatine filters. Fig. 6 shows the 
transmission curvatures of the used MKF6 filters. 
It was tried to record images in the UV- region 
(315nm - 380nm), the visible spectrum (380nm - 
780nm) and the near infrared (680nm - 1200nm). 
Given that the CCD-sensors are less sensitive in 
the UV-region, with the RSC and the CS1, only the 
acquisition of images in the visible and in the 
near infrared was possible. By using different 
films for the Rolleiflex 6006 metric, it was pos- 
sible to obtain images that covered the whole 
spectrum. Altogether, 11 different filters were 
used with the different cameras. 
During the survey, favourable constant diffuse 
illumination with little shadow prevailed. 
  
  
  
  
  
  
  
Recording | Area Image Format Pixelsize Spectral 
System [pixel] image object Range 
[um] | [mm] [nm] 
RSC 1 2048 x 2048 15 6 400-1150 
1 512 x 512 80 24 ORWO VF-45 
6006/ 1024 x 1024 40 12 330- 730 
RS1 2 510 x 512 80 55-80  |ORWO I-750 
1024 x 1024 40 20-30 680- 800 
CS1 1 768 x 512 8 21 400-1150 
2 768 x 512 8 26-36 
  
  
  
  
  
Table 1: Image Acquisition Conditions 
3.3 Image evaluation 
The multispectral image evaluation was effected 
with the software package ERDAS(©), which was 
originally developed for the evaluation of remote 
sensing data. The software is implemented at a 
SUN-workstation of the IPF at the Technical 
University Dresden. 
In a first step all corresponding images were 
geometrically transformed to a common basis. In 
order to reduce the amount of data, principal 
component transformation was effected with the 7 
- 8 original bands; for further evaluation, the 
first four synthetic channels were used. 
The spectral behaviour of the observed material 
was unknown and a determination of training- 
areas for a supervised classification became 
difficult. Therefore, for a first analyse a 
minimum distance operator was used for an 
unsupervised classification of the images.