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Proceedings Paper

Data Compression Of Multispectral Images
Author(s): G. X. Ritter; J. N. Wilson; J. L. Davidson
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Paper Abstract

Data fusion of multispectral image data requires tech-niques that are often time-consuming while giving unclear results. Development of algorithms that integrate information in a useful way is important to improving autonomous and semi-autonomous image understanding systems. This paper presents a comparison of two data fusion methods, each of which compresses the data. One method, the Hotelling transform (Karhunen-Loeve transform), is investigated and its results compared with a less computationally intensive method using new techniques. Each algorithm is translated into the Air Force's Image Algebra, as it provides a common mathematical environment for image algorithm development, optimization, comparison, coding and performance evaluation. The translucent nature of the algebra facilitates the comparison of the advantages and disadvantages of each method.

Paper Details

Date Published: 18 January 1988
PDF: 7 pages
Proc. SPIE 0829, Applications of Digital Image Processing X, (18 January 1988); doi: 10.1117/12.942108
Show Author Affiliations
G. X. Ritter, University of Florida (United States)
J. N. Wilson, University of Florida (United States)
J. L. Davidson, University of Florida (United States)


Published in SPIE Proceedings Vol. 0829:
Applications of Digital Image Processing X
Andrew G. Tescher, Editor(s)

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