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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
C. Pohl ’, H. Touron 2
1 International Institute for Aerospace Survey and Earth Sciences (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands,
2 Western European Union Satellite Centre (WEUSC), P.O. Box 511, 28850 Torrejon de Ardoz (Madrid), Spain
KEYWORDS: WEU, Visual Interpretation, Data and Image Fusion, Operationalization, Complementarity
Space-based observation provides repeated, unrestricted access to every comer of the globe, in full compliance with international
law, and this capability may provide early warning of crises before action has to be taken to deal with them. Risks can be assessed
before they turn into threats. The Western European Union Satellite Centre (WEUSC) operationally exploits imagery derived from
different Earth observation satellites for security and defence purposes. At the WEUSC, the remote sensing data is digitally processed
to enhance visual image interpretation capabilities. One of the processes applied is image fusion. This paper reports on the
experiences gained using image fusion as a tool to integrate multi-sensor images in order to benefit from increased spatial, spectral
and temporal resolution, in addition to increased reliability and reduced ambiguity. After a short introduction, the concept of image
fusion as it is implemented at the WEUSC is described, followed by an explanation of the processing involved. An overview of
operational applications using image fusion as a major step prior to visual image interpretation allows the compilation of a list of
operational statements, vital in the implementation of image fusion. A very important factor of applying image fusion is the
integration of complementary data. The complementarity of visible and infrared (VIR) with synthetic aperture radar (SAR) images is
a well known example, where the objects contained in the images are 'seen' from very different perspectives (wavelength and viewing
geometry). The integration of high resolution and multispectral information forms another type of complementarity. This paper
provides an overview of issues in operationally-used image fusion relating to the processing involved and discusses benefits and
limitations of approaches, illustrated by examples. All results have to be viewed in the context of visual image exploitation.
Multi-sensor data fusion appears to be widely recognized in the
remote sensing user community. This is obvious from the
amount of conferences and workshops focussing on data fusion,
as well as the special issues of scientific journals dedicated to
the topic. Previously, data fusion, and in particular image
fusion belonged to the world of research and development. In
the meantime it has become a valuable technique for data
enhancement in many applications. More and more data
providers envisage the marketing of fused products. Software
vendors started to offer pre-defined fusion methods within their
generic image processing packages.
The WEUSC operationally exploits images from many different
sensors available on the market in order to respond to requests
from the WEU council or WEU Member States in the fields of
general security surveillance, support for Petersberg missions 1
and surveillance in more specific spheres. In order to perform
multi-sensor image interpretation as required operationally from
the Satellite Centre, the image analysts process the imagery to
obtain enhanced and suitable image products. One of the
approaches utilized is image fusion. Tools for and training
material on image fusion have been developed and implemented
1 Petersberg missions: WEU term to describe missions ranging
from humanitarian and rescue tasks to tasks involving combat
forces in crisis management, including peacemaking.
in order to support the daily work of the image analysts. The
data fusion system (DFS) is used to benefit from improved
spectral, spatial and temporal resolution. Furthermore, the
consideration of multi-sensor data ensures the availability of
data when it is needed and the replacement of deficiencies
contained in satellite imagery (e.g. cloud cover). In addition,
image fusion can contribute to more reliability and reduced
ambiguity of the interpretation results.
The following sections provide a short description of the fusion
approach established and experiences obtained in an
operational environment illustrated by real world examples.
Image fusion aims at the integration of complementary data to
enhance the information content of the imagery, i.e. make the
imagery more useful to a particular application. From the
experiences gained in the past, it is clear that the selection of an
image fusion approach depends on the desired application. The
definition of image combinations and techniques depends on
the characteristics a dataset should have in order to serve the
user (Pohl and van Genderen, 1998). However, it is possible to
summarize a general approach, which describes the overall
processing chain needed in order to achieve image fusion (see
Fig. 1.). Basically, the dataset to be fused has to be pre-
processed in order to achieve conformity or data alignment as
defined by Wald (1998b). In the case of multi-sensor image