Contents 1. Introduction
Hyperspectral imaging is a demonstrated technology for numerous earth and space-borne applications involving tasks such as target detection, invasive species monitoring and precision agriculture. However, hyperspectral imaging suffers from the “curse of dimensionality”. Of particular interest is new theory for dimensionality reduction or identification of fewer spectral bands for multispectral versus hyperspectral imaging, typically relative to some specific task, which aids efficient computation, improves classification and lowers system cost. Most techniques can be divided into two broad categories-projection or clustering. Projection techniques require all bands initially (versus feature selection) and they are focused on reducing dimensionality. Approaches include principal component analysis (PCA), Fishers linear discriminant analysis (FLDA) and generalized discriminant analysis (GDA), random projections (RP), and kernel extensions. Some methods are unsupervised, e.g., PCA and RP, while others are supervised, e.g., FLDA and GDA. Clustering is unsupervised learning and it can be applied to hyperspectral imagery in a number of ways. While it does not automatically do dimensionality reduction, it helps to identify structure and one can take that information and use it for dimensionality reduction or band group selection. For example, in [1] Martinez et al. used an information measure to compute dissimilarity between bands and they used hierarchical clustering with Ward's single linkage to produce a minimum variance partitioning of the bands. In [2], Imani and Ghassemain used (hard) c-means for supervised band grouping. Martinez's method suffers from the limitations of vanilla hierarchical clustering, e.g., how to pick clusters from the dendogram. Imani and Ghassemain's approach suffers from the limitations of the -means clustering algorithm, e.g., initialization, selection of , and lack of ability compared to “soft” clustering (probabilistic, fuzzy or possibilistic).
