International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
mentioned and briefly discussed only for reasons of 
completeness. Although it is widely acknowledged that such 
methods do not at all fulfil serious accuracy requirements, they 
are still applied by many users in their remote sensing 
applications. Emphasis, however, will be put on methods which 
allow a precise coregistration or geocoding of images in order 
to provide optimum datasets for subsequent fusion-based 
applications. 
Suitable approaches to precisely fuse data from a geometric 
point of view are: 
• parametric methods, being based on the consideration of 
sensor-specific mapping models following photogrammetric 
techniques to transform an image to a map or vice versa. 
• coregistration techniques being independent of any sensor- 
specific imaging parameters. Here, registration can be 
performed in image or map geometry based on one and the 
same approach. 
Pre-operational and operational image coregistration and 
geocoding have been the topic of various research activities 
related to remote sensing and GIS at the Institute of Digital 
Image Processing. Illustrative examples of these activities are 
included to show the potential as well as the drawbacks of the 
various methods being described. 
2. DISPLACEMENT EFFECTS FOR MONITORING 
APPLICATIONS 
A major restriction for the application of multisensor and 
multitemporal image data for monitoring applications is the 
position accuracy when overlaying the different images. In 
order to demonstrate the necessity of high location accuracy a 
simulation has been performed to analyse displacement effects 
for forest monitoring applications. 
The simulation was carried out by shifting a forest type 
classification, for which three different forest classes have been 
derived from geocoded Landsat TM image data by a magnitude 
of 0.5 pixel each to east and north (25 m pixel footprint). 
According to many reported Landsat TM geocoding results, 0.5 
pixels can be assumed to be the optimal realistic geometric 
accuracy that can by reached by parametric methods. The 
classification error resulting from the simulated geocoding error 
can then be estimated by comparing the shifted classification 
with the original one. The class changes due to the geocoding 
error for the three forest types are listed in Table 1. 
In case of no displacement, or of no effects of the displacement, 
the change between the classes being compared should be 0%. 
In other words, the comparisons between e.g. coniferous and 
coniferous should amount to 100%. In the simulated case, 
however, only 86.74% of the coniferous pixels still belong to 
the "coniferous"-class after the displacement. This means that 
the displacement leads to an error of 13.26%. This error splits 
up into the coniferous pixels, which are incorrectly displaced 
into the class "mixed forest" (11.17%) and the coniferous 
pixels, which are incorrectly displaced into the class "deciduous 
forest" (2.09%). 
Table 1. Classification changes caused by a geocoding error 
of 0.5 pixel to east and north. 
Changes of coniferous forest 
coniferous => coniferous 
86.74 % 
coniferous => mixed 
11.17 % 
coniferous => deciduous 
2.09 % 
Changes of deciduous forest 
deciduous => coniferous 
20.18% 
deciduous => deciduous 
69.07 % 
deciduous => mixed 
10.75 % 
Changes of mixed forest 
mixed => coniferous 
8.63 % 
mixed => deciduous 
16.99% 
mixed => mixed 
74.38 % 
By shifting the forest type classification by an assumed 
geocoding error of 1 pixel each to east and north, the values in 
Table 2 were obtained. 
Table 2. Classification changes caused by a geocoding error 
of 1.0 pixel to east and north. 
Changes of coniferous forest 
coniferous => coniferous 
83.55 % 
coniferous => mixed 
13.96 % 
coniferous => deciduous 
2.49 % 
Changes of deciduous forest 
deciduous => coniferous 
25.98 % 
deciduous => deciduous 
60.07 % 
deciduous ==» mixed 
13.95 % 
Changes of mixed forest 
mixed coniferous 
10.00 % 
mixed => deciduous 
22.48 % 
mixed => mixed 
67.52 % 
These simulations show that a pixel-accuracy comparison of 
signatures or classification results cannot be recommended for 
forest monitoring applications, since changes in forest classes 
occur in many cases in subpixel dimensions. This is particularly 
true for forest types, which are characterised by heterogeneous 
spatial distribution. This can be demonstrated by the 
correspondence values of the class "mixed forest", for which 
correspondence values of only 74.38% (shift of 0.5 pixel to east 
and north) and 67.52% (shift of 1.0 pixel to east and north) 
could be noticed. 
These classification changes are just one indicator for the 
necessity of high-accuracy image geocoding. Methods to fulfil 
this demand are discussed in the following section. 
3. COREGISTRATION AND GEOCODING METHODS 
3.1. Polynomial Rectification 
Straightforward polynomial rectification and image warping 
tools have been used to register and/or rectify image data for