International Archives of Photogrammetry and Remote Sensing. Vol. XXXII Part 7C2, UNISPACE III, Vienna, 1999 
27 
I5PR5 
UNISPACE HI - ISPRS Workshop on 
“Resource Mapping from Space ” 
9:00 am -12:00 pm, 22 July 1999, VIC Room B 
Vienna, Austria 
I5PR5 
DATA FUSION FOR A BETTER EXPLOITATION OF DATA IN ENVIRONMENT AND EARTH OBSERVATION 
SCIENCES 
Lucien WALD 
Ecole des Mines de Paris, Groupe Télédétection & Modélisation 
BP 207, 06904 Sophia Antipolis cedex, France 
Phone: +33 (0)4 93 95 74 49 - Fax: +33 (0)4 93 95 75 35 - 
e-mail: lucien.wald@cenerg.cma.fr 
ABSTRACT 
Earth Observation (EO) data are currently used in several applications. Some achievements are readied which are fairly satisfactory' 
from the point of view of customers. How'ever it is recognised that each application only uses a small amount of the available EO data. 
Numerous EO missions exist. The number of sensors and of possible observations is huge. According to the instruments, the 
observations can be done in several wavelengths, (visible and infrared, thermal infrared, microwave domains), with different modes: 
passive (radiometer, spectrometer) or active (imaging radar, scatterometer, altimeter), with different spatial resolution (from 1 to 50 
km), and with various observation frequencies (from 1/4 hour to 30 days). A judicious use of the whole capabilities of EO missions and 
data should lead to an increase of the quality of the product or information delivered to the customer or of the decision taken by the 
customer. This wealth of data is presently not fully exploited by the customer, mostly because methods, techniques and tools are not yet 
available. 
Data fusion lias been defined by a group of European experts as "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". 
Three examples will be given, illustrating the benefits of data fusion relative to standard approaches. 
The first example will demonstrate how mapping features in the cities can be enhanced by the fusion of images of different spatial and 
spectral resolutions. A panchromatic image of Marseille (France) downtown with a spatial resolution close to 2 m is merged with a 
inultispectral image of lower spatial resolution. The synthesised high resolution multispectral image provides a much better description 
of the city features. 
Ground measurements are usually the only information of the distribution of pollutants. In the second example, these measurements are 
fused with satellite images in order to map the concentration in pollutants in the city of Nantes (France). Local authorities, in charge of 
pollution are supplied with a more accurate knowledge of the spatial distribution of the pollutants. 
The tliird example shows how accurate digital elevation models can be constructed from multi-sources satellite images. For each point, 
the best source is selected according to the associated error and taking into account the overall coherency of the digital elevation model.