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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part Bl. Beijing 2008 
demonstrated for the ADS40 (Reulke et al. 2006). Based on the 
sampling theorem, the optical system has to provide the 
necessary spatial frequency for Superresolution. 
Figure 4. Textured DEM (image data by MFC/DLR) 
Object detection and tracking are methods which are not only 
employed in the fields of photogrammetry and remote sensing. 
Typical applications refer to the integration of optical and 
distance sensors, which are applied in the automobile industry 
(e.g. vehicle sensors) (Catala-Prat et al., 2008), to tracking of 
objects provided by multiple camera systems for observing 
tasks and to traffic monitoring (Meffert et al., 2005). Related 
approaches are interesting as they also contain synchronization 
aspects between datasets (beside the classic spatial co 
registration). 
4. DERIVATION OF REQUIREMENTS FOR FUSION 
PRODUCTS 
Aspects of relevance to standardization and to the quality 
assessment of fusion results irrespective of which algorithm is 
applied do not appear in many published papers. The following 
fields of requirements can be determined: 
• Requirements for utilised sensors 
• Requirements for methods and quality of co 
registration 
• Requirements for spatial image quality (e.g. from 
point spread function analyses) and 
• Requirements for radiometric and colour quality (for 
true colour image data). 
The above requirements have to be specified and explained 
more exactly for individual approaches. 
As for the requirements for image and elevation data reference 
can be made to the German standard ‘Requirements for the 
orthophoto’ (German Institute for Standardization DIN, 2003). 
As for pan-sharpening methods the requirements correspond 
exemplarily to following aspects: 
• The utilized sensors have to fulfil both remote sensing 
tasks and true colour demands. 
• The quality of co-registration refers to low-resolution 
multispectral data in comparison to the high- 
resolution band. 
• The spatial image quality (e.g. from point spread 
function analysis) has to correspond with the image 
quality of the high-resolution band after the pan- 
sharpening process. 
• The radiometre and colour quality (for true colour 
image data) has to correspondent with the 
multispectral products. 
The quality aspects of pan-sharpened images are described in 
greater detail below. 
Requirements for data fusion 
• Enhancement of spatial resolution 
A quality criterion for pan-sharpening methods is the 
preservation of the spatial resolution of the 
panchromatic image in the end product. This can be 
evaluated by analysis of the point spread function by 
means of distinctive image structures. 
• Preservation of spectral features 
Spectral features of the original low-resolution image 
need to be preserved in the generated high-resolution 
multispectral image in order to be in the position to 
adopt e.g. classification algorithms successfully. A 
modification of the colour distribution in the end 
product compared to the reference image can be 
roughly endorsed by comparison of the histograms of 
the red, green and blue proportions of the individual 
images. More differentiated assessment of the 
preservation of true colour features in the original and 
the pan-sharpened images can be carried out by 
applying colour-distance in Lab-space (Wyszecki and 
Stiles, 2000). 
Another quality criterion for the pan-sharpening process 
performance is the number of visible artefacts. Especially 
problematic are object edges, if the red, green and blue bands 
are not accurately co-registered. 
5. CONCLUSIONS AND OUTLOOK 
A small number of fusion methods have been well implemented 
(e.g. orthophoto generation, pan-sharpening). 
Data fusion is not only an issue in remote sensing research. It 
has become of operational importance. Many civilian and 
defence applications are enhanced through sensor and data 
fusion (e.g. transportation management, tracking, and 
automotive). 
The quality assessment of fusion is still difficult. Corresponding 
fundamental scientific research is necessary. 
From our point of view, new applications based on higher levels 
(feature and decision level) are to develop in the future. An 
outstanding challenge is the handling of ambiguities if different 
sensors come to different fusion results on decision-level. 
ACKNOWLEDGEMENTS 
We would like to thank Christoph Doerstel from Intergraph for 
providing the DMC images and the pan-sharpened DMC 
images as well as Anko Boemer from German Aerospace 
Center/ Optical Information Systems for providing the images 
of the 3D model and the textured 3D model. Many thanks are