Hadamard-Walsh random projection for hyperspectral image classification

The rich spectral information in hyperspectral imagery gives rise to huge storage and transmission costs. Dimensionality reduction aims to reduce the space complexity in hyperspectral imagery by projecting data into a low-dimensional subspace. There has been an increasing interest in dimensionality reduction driven by random projections due to its data-independent representation as well as desirable qualities such as the preservation of important information and low computational costs. The performance of a random projection derived from a Hadamard-Walsh matrix is investigated, with experimental results demonstrating classification performance superior to other random dimensionality-reduction methods when deployed in conjunction with a composite-kernel support vector machine that exploits both spatial and spectral information for the classification of hyperspectral imagery.