2 July 1998 Projection pursuit analysis of hyperspectral scenes
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Abstract
Principal Components Analysis is very effective at compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of signal variance. Unfortunately, information content in hyperspectral images does not always coincide with such projections. We propose the application of Projection Pursuit, which seeks to find a set of orthogonal projections that are 'interesting' in the sense that they deviate from the Gaussian distribution assumption. Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections we follow a 2-step iterative process where we first search for a projection that maximizes a projection index based on the divergence of the projection's estimated probability distribution from the Gaussian distribution, and then reduce the rank by projecting the data onto the subspace orthogonal to the previous projections. To find the projection that maximizes the index, a novel approach is taken which does not use an optimization algorithm, but rather searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. This method is shown to work with simulated examples as well as data for the Hyperspectral Digital Imagery Collection Experiment.
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Agustin I. Ifarraguerri, Agustin I. Ifarraguerri, Chein-I Chang, Chein-I Chang, } "Projection pursuit analysis of hyperspectral scenes", Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); doi: 10.1117/12.312608; https://doi.org/10.1117/12.312608
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