2. EDUCATION IN GEOMETRIC IMAGE 
PROCESSING 
2.1 Teaching on reasons for geometric image 
correction 
The tradition in Hannover emphasises the role of 
geometry for the practical application of remote 
sensing techniques. 
The didactic concept is permanently adapted to 
practical requirements. It starts with pointing to the 
fact, the main reason for geometric image processing 
is, to derive geometric correct positions of the pixels, 
which contain surface related grey value information, 
in order to achieve a reliable geocoding of geometric 
distorted remote sensing image data for the 
correlation with GIS- or map-data. 
Thus the full success of a remote sensing mapping 
campaign depends on the ability of a proper 
rectification of geometric distortions, in particular, to 
avoid misinterpretations due to mismatching within a 
GIS. 
Main objectives in this context are the providings of 
suited algorithms and software, necessary to produce 
(digital) geometric corrected raster data of remote 
sensing imagery in view of 
- GIS Integration 
- mosaicing, 
- sensor comparison, 
- updating etc.. 
This task has been solved by the development and 
improvement of suited algorithms, which at least 
allow to calculate 3 dimensional and not only 2 
dimensional ground control point coordinate 
information. 
2.2 Leading to geometric approaches 
The transformation of imagedata into a map or GIS- 
coordinate system is an interpolation problem . 
The interpolation process can be based on 
- ground control point- and/or texture information, 
- housekeeping data (like airborne GPS etc.) and on 
- combinations of both types of data. 
Ground control point data is derived from known 
numeric coordinate values, from GPS, from maps 
and/or from imagecoordinates of corresponding 
points, which can be verified manually, but 
increasingly by interactive digital (relative and 
absolute) correlation techniques. 
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There are in principal 3 categories of algorithms to 
solve the geometric problems for remote sensing 
imagery: 
- non-parametric interpolation methods 
- (physical) parametric methods and 
- combined approaches. 
Non-parametric interpolation methods are namely 
- polynomial equations, 
- Spline functions, 
- interpolation in a stochastic field, like moving 
average, weighted mean, linear prediction etc.. 
For images of flat terrain 2 dimensional heuristic 
polynomial equations for limited areas and stable 
flight conditions for some cases already allow to 
obtain sufficient accuracies for geometric 
calculations. 
The advantages of 2 dimensional polynomial 
equations beside others are 
- the didactic value for introducing into digital 
geometric image processing, 
- suitability for quick programming 
- satisfying for limited areas of flat terrain 
- support for approximate value determination 
- support for blunder detection 
Some disadvantages of 2 dimensional polynomials 
are 
- arbitrariness 
- limited area of validation 
- a blockadjustment based on arbitrary polynomial 
equations of higher than first order shows an extreme 
bad error propagation. 
- restriction for 2 dimensions. 
2.3 Education in improvement of algorithms 
GIS systems still lack suited algorithms for geometric 
as well as for radiometric manipulation of the data. 
The development of suited algorithms for GIS 
systems is still a real market gap. Therefore it is one 
intention of the courses of study of remote sensing at 
the University of Hannover, to prepare the students 
for algorithm development and improvement. 
In this context the formulation of strict geometric 
algorithms for remote sensing imagery is a typical 
sample for algorithm development. From the basic 
idea a physical parametric solution is envisaged, 
which allows to calculate 
- the global and local behaviour of the sensor 
position and attitudes, 
- 3dimensional ground control point coordinates and 
- computation of imagecoordinates for 3 dimensional 
output raster data (resampling).