habbar bleibt. Die Methode wird an mehreren Beispielen er- 
    
  
    
  
  
  
  
  
  
  
   
   
   
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
    
probt. 
1. Introduetion 
With multispectral scanning of the earth's surface from sat- 
ellites or airplanes, mean radiation intensities of areal 
elements are measured in several fixed wave-lengths ranges 
and digitized. Considering the fact of equal radiation fea- 
tures for equal topographic objects, one can conclude from 
these spectra to the topographic objects producing them, 
provided that size and amount of the selected frequency win- 
dows are sufficient. This procedure of multispectral classi- 
fication leads possibly together with other recognition al- 
gorithms by means of digital image processing systems to the 
automatic output of land use maps. The separability of ob- 
jects, however, is considerably determined, as already im- 
plied above, by the non-linear dependency of the multispec- 
tral data between themselves. For this reason, it is neces- 
sary to reduce them to the significantly different feature 
vectors (data vectors of the spectral data sets), thus also 
accelerating their processing on the computer. The computa- 
tion of correlation coefficients (and procedures derived 
thereof [1]) between the feature vectors does not yield the 
desired information on spectral differentiations in detail, 
because these statistic methods suppress differences for 
those objects the portion of which is only small in the to- 
tal data quantity, but the importance of which can be essen- 
tial for eartographie purposes (e.g. roads, hydrography). 
For this reason, a method is applied covering each pixel and 
representing it in correct position, if its value exceeds a 
threshold (Student-t-value) determined by least squares ad- 
justment. Thus, from a noisy structure of these pixels a 
linear dependency can be concluded. From a systematic struc- 
ture (linear or areal) follows that the pixels do not depend 
on the object and its position.