International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
125 
IRS-1C PAN). It has been shown that even the fusion of 
spatially very different datasets can result in increased 
interpretability; an operational example is the use of space 
photography and pushbroom scanners, e.g. Russian imagery & 
SPOT XS. 
The challenges faced in the resolution merge are often the large 
difference in spatial resolution. Combining images of resolution 
differences of more than 1:10 causes a number of problems. 
Starting with the difficulty in identifying control points in the 
data pair, the images are difficult to be co-registered. In this 
case, points or features have to be selected with care, due to the 
additional large difference in viewing geometry of the sensors 
involved. In the case of large spatial or spectral differences in 
images to be co-registered, the identification of features (lines 
or areas) leads to more accurate results than the measurement of 
points only. For this reason, the WEUSC has implemented a 
tool that allows the identification of similar features in two data 
sources by on-screen digitizing. Those features are used to 
calculate a transformation model to co-register the data. This 
approach leads to better results in terms of geometric accuracy 
than the application of polynomials based on tie point 
measurements. 
Depending on the further use of the fused product, the 
resolution difference of 1:10, as it is the case for a combination 
of SPOT XS (20 m) and Russian imagery (2 m), still results in a 
suitable product. For visual purposes, the resulting fused image 
provides valuable information that the human interpreter can 
exploit considering eventually occurring errors due to 
misregistration. 
In spectral terms, the fusion of panchromatic and multispectral 
imagery does not invoke particular problems, due to the similar 
nature of the images involved. This matter becomes more 
critical when fusing VIR and SAR. In a case study conducted in 
the area of Madrid, Spain, the aim of introducing spectral 
information in high resolution imagery has been achieved. The 
fused product has been obtained using a linear combination of 
the multispectral SPOT (20 m) image, resampled to the pixel 
resolution of the scanned Russian photograph (2 m), and 
introducing weighting factors to balance the contribution of 
each sensor to the benefit of the interpretability of the result. 
Minor problems were faced regarding the shadows in the 
Russian photography, due to the appearances of high buildings 
in the city centre of Madrid. Nevertheless, the fused product 
immediately discloses the different appearance of the new part 
of the town (different texture and more red) in contrast to the 
old city centre (less red). The difference in the spectral response 
lies in the fact that the old city contains buildings with different 
roof shapes and materials. The user still has the possibility to 
assign different colour bands to the RGB colour composite, 
which leads to different representations of the same data. This 
becomes relevant when the results are presented to decision 
makers, who are generally not familiar with false colour 
composites. Therefore, the closer the images get to ’natural’ 
appearance, the better. 
Another effect to be mentioned is the relief effects, which 
played an important role in another work performed in the area 
of Goma, Congo. The presence of relief introduces geometric 
distortions in the imagery which have to be taken into account 
in the registration process. Depending on the sensor type and 
the type of relief, the distortion varies and can reach 
unacceptable ranges so that the use of a digital elevation model 
(DEM) in the rectification process becomes vital. In cases of 
moderate terrain height variations, the selection of small subsets 
using local geometric models for co-registration avoids strong 
distortions occurring with global models for the whole scene. In 
principle, a co-registration accuracy of < 1 pixel is desirable. 
The town of Goma, Congo (population 250,000) is situated on 
the northern shore of Lake Kivu, adjacent to the border between 
Congo and Rwanda. It became a key element of the refugee 
crisis during November 1996 (Pohl et al., 1997). The 
appearance of the refugee camps in one of the SPOT images is 
marked by their clear-cut boundaries with the surrounding 
terrain and by the presence of internal roads. Mugunga, the 
refugee camp discussed in this paper, also shows an area which 
appears to lack any internal infrastructure, suggesting it is an 
overspill area formed after the original capacity of the camp has 
been exceeded. Fusion of the SPOT PAN and XS data via the 
IHS transformation facilitated the location and description of 
the camp. It preserved the spectral contrast with respect to 
surrounding terrain, showing the details of the internal layout 
seen in the panchromatic data. The water ponds inside the camp 
were identifiable in the fused image by their distinct blue 
colour. 
The third example of successfully used resolution merges at the 
WEUSC is the city of Mostar, former Yugoslavia. Again the 
challenge in a SPOT XS and Russian data merge has been the 
large difference in spatial resolution for the identification of 
GCPs. The result immediately puts the objects into their correct 
context due the presence of high resolution and colour at the 
same time. Adding the third dimension to this fused image in 
terms of a DEM, a 3D perspective can be created which helps 
the human interpreter to understand the environment of this site. 
Three-dimensional representations can be used to support the 
interpretation itself and to illustrate the results to an end-user 
(Prisco et al., 1997). 
3.2. Multi-sensor (VIR/SAR) Fusion 
Due to the disparate nature of optical and microwave sensors, 
their imagery depicts different characteristics of the features 
observed. In combining these images, the user obtains a more 
complete view of the object of interest, which might lead to 
improved understanding of the environment being looked at. 
SAR data alone is normally very difficult to be visually 
interpreted, since it does not correspond to the human visual 
perception as do optical images. However, in combination with 
VIR images, SAR data can reveal very valuable information to 
the human interpreter. In areas of saturation of optical images, 
SAR images can visualize structural components. Typical 
comer reflectors allow certain conclusions in optical data as 
well (e.g. the detection of power lines, high man-made 
structures, fences, etc.).