International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
need for a conceptual framework was clearly expressed by 
scientists as well as practitioners. 
It is often written that fusion takes place at three levels in data 
fusion: pixel, attribute and decision (Brandstàtter and Sharov, 
1998; Csathô and Schenk, 1998; Mangolini, 1994; Pohl and 
van Genderen, 1998). It presents two drawbacks. The word 
"pixel" is inappropriate here ; the pixel is only a support of 
information and has no more semantic significance than voxel 
or «-dimension cell. Measurements or observations or signal 
would be more appropriate. But even if “pixel” is corrected and 
though the authors understand very well data fusion, my own 
experience in teaching shows that such a categorization, also 
proposed by Klein (1993) or Hall, Llinas (1997) may be 
misleading and should be avoided. It may falsely imply that 
fusion processes do not deal simultaneously with these different 
levels. Usually, fusion of measurements results into attributes, 
and fusion of attributes into decisions, but it may be different. 
In Earth observation domain, one may use some features 
(attribute level) held in a geographical information system to 
help in classifying multispectral images (measurement level) 
provided by several sensors. In this particular case, some data 
are measurements of energy, and others may be symbols. Inputs 
of a fusion process can be any of the levels above-mentioned, in 
a mixed way, and outputs can be any of these levels. Consider 
the case of the ARSIS concept which increases the spatial 
resolution of a multispectral image given another image of a 
better resolution not necessarily acquired in the same spectral 
bands (Ranchin et al, 1996, Ranchin, Wald, 1998). It intends 
to simulate what would be observed by a multispectral sensor 
having a better spatial resolution. Accordingly, it simulates 
measurements through a fusion process and inference models. 
The formalism of Houzelle, Giraudon (1994) is preferable. It 
allows all semantic levels (measurements, attributes, decisions) 
to be simultaneous inputs of a fusion process. Wald (1998a) 
presented several examples of this formalism applied to remote 
sensing. 
A search for a more suitable definition was launched with the 
following principles. The definition for data fusion should not 
be restricted to data output from sensors (signal). It should 
neither be based on the semantic levels of the information. It 
should not be restricted to methods and techniques or 
architectures of systems, since we aim at setting up a 
conceptual framework for data fusion. Based upon the works of 
Buchroithner (1998) and Wald (1998b), the following 
definition was adopted in January 1998: « data fusion is a 
formal framework in which are expressed means and tools for 
the alliance of data originating from different sources. It aims at 
obtaining information of greater quality; the exact definition of 
‘greater quality’ will depend upon the application ». (in French: 
la fusion de données constitue un cadre formel dans lequel 
s’expriment les moyens et techniques permettant l’alliance des 
données provenant de sources diverses. Elle vise à l’obtention 
d’information de plus grande qualité ; la définition exacte de 
« plus grande qualité » dépendra de l’application.) 
This definition is clearly putting an emphasis on the framework 
and on the fundamentals in remote sensing underlying data 
fusion instead on the tools and means themselves, as is done 
usually. The latter have obviously strong importance but they 
are only means not principles. A review of methods and tools 
can be found in Pohl and van Genderen (1998), US Department 
of Defence (1991). 
Secondly, it is also putting an emphasis on the quality. This is 
certainly the aspect missing in most of the literature about data 
fusion, but one of the most delicate. Here, quality has not a very 
specific meaning. It is a generic word denoting that the 
resulting information is more satisfactory for the « customer » 
when performing the fusion process than without it. For 
example, a better quality may be an increase in accuracy of a 
geophysical parameter or of a classification. It may also be 
related to the production of a more relevant information of 
increased utility, or to the robustness in operational procedures. 
Greater quality may also mean a better coverage of the area of 
interest, or a better use of financial or human resources allotted 
to a project. 
In this definition, spectral channels of a same sensor are to be 
considered as different sources, as well as images taken at 
different instants. Hence, any processing of images acquired by 
the same sensor is relevant to the data fusion domain, such as 
classification of multispectral imagery, or computation of the 
NDVI (normalized difference vegetation index), or atmospheric 
correction of spectral bands using other bands of the same 
sensor. Any processing of time-series of data acquired by the 
same sensor or different sensors, is a fusion process. 
3. OTHER DEFINITIONS 
It then has been suggested to use the terms merging and 
combination in a much broader sense than fusion, with 
combination being even broader than merging. These two terms 
define any process that implies a mathematical operation 
performed on at least two sets of information. These definitions 
are intentionally loose and offer space for various 
interpretations. Merging or combination are not defined with an 
opposition to fusion. They are simply more general, also 
because we often need such terms to describe processes and 
methods in a general way, without entering details. Integration 
may play a similar role though it implicitly refers more to 
concatenation (i.e. increasing the state vector) than to the 
extraction of relevant information. 
Another domain pertains to data fusion: data assimilation or 
optimal control. Data assimilation deals with the inclusion of 
measured data into numerical models for forecasting or analysis 
of the behavior of a system. A well-known example of a 
mathematical technique used in data assimilation is the Kalman 
filtering. Data assimilation is used daily for weather forecasting. 
Terms like measurements, attributes, rules or decisions, are 
often used in data fusion. These terms as well as others related