Discriminant Analysis-Based Dimension Reduction for Hyperspectral Image Classification: A Survey of the Most Recent Advances and an Experimental Comparison of Different Techniques

Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral signatures, making it possible to distinguish materials through subtle spectral discrepancies. Because these spectral bands are highly correlated, dimensionality reduction, as the name suggests, seeks to reduce data dimensionality without losing desirable information. This article reviews discriminant analysisbased dimensionality-reduction approaches for hyperspectral imagery, including typical linear discriminant analysis (LDA), state-of-the-art sparse graph-based discriminant analysis (SGDA), and their extensions.