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8 July 1994 Design of optimal transformations for multispectral change detection using projection pursuit
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Abstract
Effective change detection techniques for the automated detection of changes of interest have been an elusive goal for many years. The problem has never been one of detecting changes but, rather one of finding the changes of most interest among all the spurious changes. Indeed, changes of interest for one application can be completely different than the changes of interest for another application. In this paper we present a brief overview of techniques to suppress changes between scenes due to different collection conditions. Techniques for sorting the detected multispectral changes according to the intended application and relating them to actual changes on the ground are presented. Our approach is to use multispectral data transformations designed by the use of a visualization tool called Projection Pursuit. This tool allows the user to design a projection of the data into a vector space specifically designed to accentuate the visibility of the changes of interest. Hence, for change detection interactive analysis, projection pursuit offers the important advantage of being able to find the optimum projection without requiring a priori information from the image analyst and requiring little human intervention. This algorithm is complemented by canonical and principal component transformations tailored for specific exploitation requirements. The approach allows design of custom change detection products for a wide variety of applications including: military, economic, and environmental. This capability reduces the burden of data manipulation decisions required of the analyst, while still providing the flexibility required for the demands of exploitation.
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Michael E. Bullock, Tim J. Patterson, and Scott R. Fairchild "Design of optimal transformations for multispectral change detection using projection pursuit", Proc. SPIE 2231, Algorithms for Multispectral and Hyperspectral Imagery, (8 July 1994); https://doi.org/10.1117/12.179770
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