' chosen 
ppect fo 
e optiori- 
IC various 
jo first 
he the- 
'elatively 
“ically 
Cc 
)S in- 
Lk 
Y(X5, Y.) 
ements 
non-para- 
jor orien- 
ints, 
) not 
"esented 
‘ation x,y 
red data 
molation 
solved 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
MULTI TEMPORAL 
MULTI-SENSOR IMAGERY 
CARTOGRAPHIC 
DATA BASES RADIONETRIC e| | e MSS IMAGERY 
© AERIAL PHOTOGRAPHY 
e TOPOGRAPHY e IR LINE SCANNER 
GEOMETRIC € RADAR (SLAR,SAR) 
CORRECTIONS 
RANGE OF 
9 BOUNDARTES DATA PROCESSING 
e SOIL TYPES GEOMETRIC TRANSFORMATION 
ie INTO THE DATA BASE SYSTEM 
: RELEVANT 
e 
ANCILLARY DATA ORT 
e GEOLOGY MULTI-SENSOR 
SELECTION OF OBJECTSIGNATURES 
e LAND USE TRAINING SITES AND -TEXTURES 
  
  
  
| OBJECT RECOGNITION AND CLASSIFICATION | 
  
  
  
  
i i 
UPDATING OF D/A CONVERSION (FILMWRITE) 
DATA BASES (E.G.LAND USE MAP) 
  
  
  
  
  
  
Figure 1 Combined processing of data bases and integrated imageries 
Finally, the interpolation of a point x,y is carried out by using the 
equation 
= n 
7=2z+DK=-2+ EX k f(s.) (5) 
i=1 J J 
J 
2.3 Multiquadric geometric processing 
  
The determination of the geometric transformation functions X and Y 
is performed in two steps. 
First, trend functions Xi" X > Y) and Your Yt (X5, Y.) are derived 
as twodimensional polynomials of low order or the sensor modelling function, 
respectively. These trend functions eliminate global distortions, scale affi- 
nities, translations and common rotations between the two coordinate systems. 
Second, remaining differential discrepancies X E X7 X41Tk and 
Y ke Y Ak. Yu at the pass points k-1,K are modelled analÿKicaily as 
multiquadric equations. The final interpolated coordinates become then 
Xi- X, * XT and Yi7 Yi! * Yir 
15 
| e iU CM — OE