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An information theoretic comparison of projection pursuit and principal component features for classification of Landsat TM imagery of central Colorado

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Projection pursuit (PP) and principal component analysis (PCA) projections derived from Landsat Thematic Mapper (TM) imagery of central Colorado were compared. While PCA is a simple subset of the general class of PP algorithms, it cannot distinguish Gaussian from non-Gaussian distributions, since it maximizes projected variance. PP algorithms, which maximize higher-order statistics, can be used to find skew or multi-modal projections in order to reveal underlying class structure. These data projections have greater fidelity to underlying land-cover distributions. On sequestered test data, PP projections improved separation of individual categories from a few percent to as much as 24%. PP performance exceeded that of PCA for all but one of the 14 land-cover categories.
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Document Type: Research Article

Affiliations: Naval Research Laboratory, Remote Sensing Division, Code 7255, Washington, DC 20375, USA

Publication date: 2000-10-15

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