Probabilistic Classification of Hyperspectral Images by Learning Nonlinear Dimensionality Reduction Mapping

Abstract

In this paper, we combined the application of a non-linear dimensionality reduction technique, isomap, with expectation maximisation in graphical probabilistic models for learning and classification of hyperspectral image. Hyperspectral image spectroscopy gives much greater information content per pixel on the image than a normal colour image. This should… (More)
DOI: 10.1109/ICIF.2006.301586

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