les 
o LOT DIRECTION DEPENDANT CLASSIFICATION OF AIRBORNE MULTISPECTRAL 
nis par SCANNER DATA 
oxception 
Lure do BERTHOLD PFEIFFER - 
civées, E 
:indements Institut für Photogrammetrie und Topographie 
"tive. Universität Karlsruhe 
ENGLERSTRASSE 7 
7500 KARLSRUHE GERMANY 
ABSTRACT 
un tra- Tae 
'océdé. The classification of airborne multispectral scanner data over the entire scan angle yields 
onse : 
misclassifications mainly near the edges, due to non-representative statistical values. The 
tent des analysis of data show object dependant brightness- and hue-shifts with scan angle, which 
: d vie : : . aat 
Se garcer can sufficiently be approximated by second orderpolynomials. This results indicate , that 
ion des the direction dependant radiance of objects must be considered in the classification of 
es airborne data. 
Guvont The maximum likelihood algorithm requires statistical values (means, covariance matrices) 
eS canaux representative for the actual scan position. To reach this goal, training fields regularly 
distributed over the strip are used to determine first or second order polynomials for means 
and covariance matrices for each class and wavelength. Using these polynomials values 
  
for every scan position are calculated and applied during classification .Direction dependant 
classification yields a marked improvement with homogeneous classification over the entire 
strip, without errors near the edges. 
  
  
I. INTRODUCTION 
The direction dependant radiance behaviour of natural objects cause a considerable bright- 
ness variation with view angle in airborne multispectral scanner deta. The evaluation of 
such data, e.g. the multispectral classification leads to misclassifications near the edges. 
Therefore in most cases only the central part of the image is classified. To evalute the 
entire strip, possibilities for direction dependant classification should be developed. 
A modification of the maximum likelihood classification is suggested, which varies the 
statistical values with scan angle. This method is independant of the data source and could 
also be applied to radar data. 
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