satellite sensors with only panchromatic (e.g., WorldView-1) or only multispectral (e.g.,
RapidEye) information are involved. Consequently, efforts started in the late 1980s to
develop methods for merging or fusing panchromatic and multispectral image data to
form multispectral images of high geometric resolution. In this chapter, we will
investigate to what degree fusion techniques can be used to form multispectral images
of larger scale when combined with high-resolution black-and-white images.
2.2 FUSION METHODS
Similar to the term scale, the word fusion has different meanings for different
communities. In a special issue on data fusion of the International Journal of
Geographical Information Science(IJGIS), Edwards and Jeansoulin (2004, p. 303)
state that “data fusion is a complex process with a wide range of issues that must be
addressed. In addition, data fusion exists in different forms in different scientific
communities. Hence, for example, the term is used by the image community to
embrace the problem of sensor fusion, where images from different sensors are
combined. The term is also used by the database community for parts of the
interoperability problem. The logic community uses the term for knowledge fusion.”
Consequently, it comes as no surprise that several definitions for data fusion can be
found in the literature. Pohl and van Genderen (1998, p. 825) proposed that “image
fusion is the combination of two or more different images to form a new image by
using a certain algorithm.” Mangolini (1994) extended data fusion to information in
general and also refers to quality. He defined data fusion as a set of methods, tools and
means using data coming from various sources of different nature, in order to increase
the quality (in a broad sense) of the requested information (Mangolini, 1994).
Hall and Llinas (1997, p. 6) proposed that “data fusion techniques combine data
from multiple sensors, and related information from associated databases.” However,
Wald (1999) argued that Pohl and van Genderen’s definition is restricted to images.
Mangolini’s definition puts the accent on the methods. It contains the large diversity
of tools but is restricted to these. Hall and Llinas refer to information quality in their
definition but still focus on the methods.
The Australian Department of Defence defined data fusion as a “multilevel,
multifaceted process dealing with the automatic detection, association, correlation,
estimation, and combination of data and information from single and multiple
sources” (Klein, 2004, p. 52). This definition is more general with respect to
the types of information than can be combined (multilevel process) and very popular
in the military community. Notwithstanding the large use of the functional model, this
definition is not suitable for the concept of data fusion, since it includes its
functionality as well as the processing levels. Its generalities as a definition for
the concept are reduced (Wald, 1999). A search for a more suitable definition was
launched by the European Association of Remote Sensing Laboratories (EARSeL)
and the French Society for Electricity and Electronics (SEE, French affiliate of the
Institute of Electrical and Electronics Engineers) and the following definition was
adopted in January 1998: “Data fusion is a formal framework in which are expressed
14
SCALE ISSUES IN MULTISENSOR IMAGE FUSION
RapidEye) information are involved. Consequently, efforts started in the late 1980s to
develop methods for merging or fusing panchromatic and multispectral image data to
form multispectral images of high geometric resolution. In this chapter, we will
investigate to what degree fusion techniques can be used to form multispectral images
of larger scale when combined with high-resolution black-and-white images.
2.2 FUSION METHODS
Similar to the term scale, the word fusion has different meanings for different
communities. In a special issue on data fusion of the International Journal of
Geographical Information Science(IJGIS), Edwards and Jeansoulin (2004, p. 303)
state that “data fusion is a complex process with a wide range of issues that must be
addressed. In addition, data fusion exists in different forms in different scientific
communities. Hence, for example, the term is used by the image community to
embrace the problem of sensor fusion, where images from different sensors are
combined. The term is also used by the database community for parts of the
interoperability problem. The logic community uses the term for knowledge fusion.”
Consequently, it comes as no surprise that several definitions for data fusion can be
found in the literature. Pohl and van Genderen (1998, p. 825) proposed that “image
fusion is the combination of two or more different images to form a new image by
using a certain algorithm.” Mangolini (1994) extended data fusion to information in
general and also refers to quality. He defined data fusion as a set of methods, tools and
means using data coming from various sources of different nature, in order to increase
the quality (in a broad sense) of the requested information (Mangolini, 1994).
Hall and Llinas (1997, p. 6) proposed that “data fusion techniques combine data
from multiple sensors, and related information from associated databases.” However,
Wald (1999) argued that Pohl and van Genderen’s definition is restricted to images.
Mangolini’s definition puts the accent on the methods. It contains the large diversity
of tools but is restricted to these. Hall and Llinas refer to information quality in their
definition but still focus on the methods.
The Australian Department of Defence defined data fusion as a “multilevel,
multifaceted process dealing with the automatic detection, association, correlation,
estimation, and combination of data and information from single and multiple
sources” (Klein, 2004, p. 52). This definition is more general with respect to
the types of information than can be combined (multilevel process) and very popular
in the military community. Notwithstanding the large use of the functional model, this
definition is not suitable for the concept of data fusion, since it includes its
functionality as well as the processing levels. Its generalities as a definition for
the concept are reduced (Wald, 1999). A search for a more suitable definition was
launched by the European Association of Remote Sensing Laboratories (EARSeL)
and the French Society for Electricity and Electronics (SEE, French affiliate of the
Institute of Electrical and Electronics Engineers) and the following definition was
adopted in January 1998: “Data fusion is a formal framework in which are expressed
14
SCALE ISSUES IN MULTISENSOR IMAGE FUSION