Overcoming the Small Sample Size Problem in Hyperspectral Classification and Detection Tasks

Hyperspectral signatures provide a dense recording of reflectance values over a wide region of the spectrum. This potentially increases the class separation capacity of the data as compared to gray scale imagery (where most of the class specific information is extracted from spatial relations between pixels) or multi-spectral imagery (where reflectance values at a few spectral bands are recorded). Availability of this rich spectral information has made it possible to design classification systems that can perform ground cover classification and target recognition very accurately. However, this advantage of hyperspectral data is typically accompanied by the burden of requiring large training sets. Another ramification of having a high dimensional feature space is over-fitting of decision boundaries by classifiers, and consequently, poor generalization capacity. In this paper, we will analyze and quantify the classification performance (as represented by overall and target recognition accuracies and false alarm rates) using various popular dimensionality reduction techniques. In particular, we will study the efficacy of PCA, Regularized LDA and Stepwise LDA in a single-classifier framework, and the efficacy of LDA in a multi-classifier, decision fusion framework.