Image Processing Reference
In-Depth Information
Chapter Summary
The main objective of data fusion is use a set of datasets to obtain information
of greater quality than what could be obtained by each single data considered
separately. It 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. Several fusion cases studies were discussed in
this chapter to illustrate the potential of data fusion techniques. The increasing
complexity of the examples is designed to gradually help students understand
data fusion. The diversity of data fusion is so important that the few examples
provided cannot fully describe its complexity. This field is still a strong and
active research in urban remote sensing and the other civilian domains.
LEARNING ACTIVITIES
Data and Image Fusion, and Software
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For a better understanding of what data fusion is and what it does.
http://www.data-fusion.org
The Online Resource for Research in Image Fusion.
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http://www.imagefusion.org
The IEEE Geoscience and Remote Sensing Society Data Fusion Committee
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(DFC).
http://www.grss-ieee.org/community/technical-committees/data-fusion/
The International Society for Information Fusion.
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http://www.inforfusion.org
Free trial version of ENVI software from Research Systems Inc limited to 7 min
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of use. Contains a set of sharpening algorithms.
http://www.rsinc.com/download/index.asp
The wavelet digest with access to information, preprints, softwares, etc.
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http://www.wavelet.org
Study Questions
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What are the different image fusion algorithms? Discuss their advantages and
disadvantages.
How do you quantitatively evaluate the quality of an image fusion product?
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