Data Compression Of Multispectral Images
Gerhard X. Ritter, Joseph N. Wilson, J. L. Davidson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1988
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.