Unsupervised hyperspectral image analysis with projection pursuit

Principal components analysis (PCA) is effective at compressing information in multivariate data sets by computing orthogonal projections that maximiz e the amount of data variance. Unfortunately, information content in hypers pectral images does not always coincide with such projections, We propose a n application of projection pursuit (PP), which seeks to find a set of proj ections that are "interesting," in the sense that they deviate from the Gau ssian distribution assumption. Once these projections are obtained, they ca n be used for image compression, segmentation, or enhancement for visual an alysis, To find these projections, a two-step iterative process is followed where we first search for a projection that maximizes a projection index b ased on the information divergence of the projection's estimated probabilit y distribution from the Gaussian distribution and then reduce the rank by p rojecting the data onto the subspace orthogonal to the previous projections , To calculate each projection, we use a simplified approach to maximizing the projection index, which does not require an optimization algorithm, it searches for a solution by obtaining a set of candidate projections from th e data and choosing the one with the highest projection index. The effectiv eness of this method is demonstrated through simulated examples as well as data from the hyperspectral digital imagery collection experiment (HYDICE) and the spatially enhanced broadband array spectrograph system (SEBASS).