INDEPENDENT COMPONENT DISCRIMINANT ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

In this paper, the use of Independent Component Discriminant Anal-ysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the ap-plication of a Bayesian classification rule on a signal composed by independent components. The method is based on the use of Inde-pendent Component Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. Then, a non parametric estimation of the density function is com-puted for each independent component. Finally, the Bayes rule is applied for classification assignment. The obtained results are com-pared with one of the most used classifier of hyperspectral images (Support Vector Machine) and show the comparative effectiveness of the proposed method