International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
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Reference image: Geocoded Landsat TM, band 2 
Input image: SPOT XS, band 1 
Coregistered SPOT image 
Fig. 13. MBC of SPOT XS to a geocoded Landsat TM image. 
5. OVERLAY AND MERGING OF GEOCODED 
MULTISENSOR IMAGES 
In this section, examples of straightforward combination of 
multitemporal and multisensor/multifrequency SAR image data 
are presented. It is shown, that just by proper visualisation of 
multiple image products the information content of the 
individual images can be partly fused and provide an extended 
information content from a visual point of view. Figure 14 and 
Figure 15 show false colour composites through super 
imposition of multisensor SAR images from the ERS, JERS and 
Radarsat and of a multitemporal ERS image dataset, 
respectively, all of them acquired over an area south of Graz. 
Figures 16 and 17 show an example of merging ERS images 
acquired from ascending and descending orbits (cf. section 4.2). 
In Figure 16 the contribution of the two images is shown in red 
and green. In general, the reliable image content of each image 
is maintained, whereas the areas which are affected by layover 
pixels in one image are replaced through image content from the 
other one. As Figure 17 shows, merging provides SAR 
backscatter information almost for the entire area, as only some 
valleys and mountain ridges of this high-mountainous area do 
not have reasonable backscatter values due to common layover 
areas (having bright grey values). 
6. CONCLUSIONS 
The fusion of multitemporal / multisensor remote sensing 
images can be efficiently done, only if the data refer to a 
common geometry. An overview of methods that may be 
seriously considered for geometric coregistration and geocoding 
has been given in section 3. As described there, multiple images 
may be registered 
• either in image geometry with regard to a selected reference 
image 
• or in map geometry with regard to a selected map projection 
or an already geocoded image. 
Until now, geocoding of the individual multisensor images to 
the geometry of a topographic map is the most usual procedure 
to accomplish comparability. However, the experiences from 
past applications and the development and implementation of 
sensor-specific parametric modelling procedures have shown 
that optimum conditions are still sometimes missing. Although 
being a straightforward procedure in general, geocoding of 
multisensor images involves certain problems and 
inconveniences, as follows: 
• The ancillary information which is delivered with the image 
data is not accurate enough to allow end-to-end geocoding 
without interaction and optimisation. More precise image 
acquisition parameters would reduce the need of control 
points to a minimum and lead to a reduction of interactive 
processes. 
• Further, the ancillary data (as well as the image data) do not 
have standardised and well documented formats. This 
problem is reduced, as commercial software to read the 
various data formats becomes increasingly available. 
Considerable developments related to this aspect are also 
made within the RSG software package. 
• For geometric registration and geocoding, reference data 
like control points, topographic maps, digital elevation 
maps etc. are frequently needed. These are not always up-to- 
date concerning accuracy and detail of information and 
therefore do not always fulfil the demands.